"
+ for i in range(len(protein.sequence)):
+ inputs += "" + protein.sequence[i]
+ inputs += ""
+ output = self.tokenizer(
+ inputs,
+ truncation=True,
+ add_special_tokens=self.add_special_tokens,
+ )
+ return output
+
+ def decode(self, inputs: str) -> Protein:
+ inputs = inputs.lstrip("").rstrip("").replace("", "").replace(" ", "")
+ return Protein.from_fasta(inputs)
+
+class BioT5(TextBasedMoleculeEditingModel, MoleculeCaptioningModel, TextGuidedMoleculeGenerationModel, TextBasedProteinGenerationModel, ProteinQAModel, MoleculeQAModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(BioT5, self).__init__(model_cfg)
+ if not os.path.exists(model_cfg.hf_model_name_or_path):
+ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+ logging.info("Repo not found. Try downloading from snapshot")
+ snapshot_download(repo_id="QizhiPei/biot5-base", local_dir=model_cfg.hf_model_name_or_path, force_download=True)
+ self.main_model = T5ForConditionalGeneration.from_pretrained(model_cfg.hf_model_name_or_path)
+ self.tokenizer = T5Tokenizer.from_pretrained(model_cfg.hf_model_name_or_path)
+ self.featurizers = {
+ "molecule": MoleculeTransformersFeaturizer(
+ tokenizer=self.tokenizer,
+ max_length=model_cfg.smiles_max_length,
+ base='SELFIES',
+ ),
+ "protein": BioT5ProteinFeaturizer(
+ model_name_or_path=model_cfg.hf_model_name_or_path,
+ max_length=model_cfg.protein_max_length,
+ add_special_tokens=True,
+ ),
+ "text": TextTransformersFeaturizer(
+ tokenizer=self.tokenizer,
+ max_length=model_cfg.text_max_length,
+ )
+ }
+ self.collators = {
+ "molecule": DataCollatorWithPadding(self.tokenizer, padding=True),
+ "protein": DataCollatorWithPadding(self.tokenizer, padding=True),
+ "text": DataCollatorWithPadding(self.tokenizer, padding=True),
+ }
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+ def _forward(self, input: BatchEncoding, label: BatchEncoding) -> torch.Tensor:
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**input).last_hidden_state)
+ return self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=input.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss
+
+ def _predict(self, input: BatchEncoding) -> torch.Tensor:
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**input).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=input.attention_mask,
+ **self.config.predict.todict(),
+ )
+ return self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+
+ def forward_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ return {"loss": self._forward(concatenate_tokens([molecule, text]), label)}
+
+ @torch.no_grad()
+ def predict_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ ) -> List[Molecule]:
+ preds = self._predict(concatenate_tokens([molecule, text]))
+ return [Molecule.from_smiles(sf.decoder("".join(sel.split(" ")))) for sel in preds]
+
+ def forward_molecule_captioning(self,
+ molecule: Featurized[Molecule],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ return {"loss": self._forward(molecule, label)}
+
+ @torch.no_grad()
+ def predict_molecule_captioning(self,
+ molecule: Featurized[Molecule],
+ ) -> List[Text]:
+ preds = self._predict(molecule)
+ return [Text.from_str(text) for text in preds]
+
+ def forward_text_guided_molecule_generation(self,
+ text: Featurized[Text],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ return {"loss": self._forward(text, label)}
+
+ @torch.no_grad()
+ def predict_text_guided_molecule_generation(self,
+ text: Featurized[Text],
+ ) -> List[Molecule]:
+ preds = self._predict(text)
+ return [Molecule.from_selfies("".join(sel.split(" "))) for sel in preds]
+
+ def forward_molecule_question_answering(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ return {"loss": self._forward(concatenate_tokens([molecule, text]), label)}
+
+ @torch.no_grad()
+ def predict_molecule_question_answering(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text]
+ ) -> List[Text]:
+ preds = self._predict(concatenate_tokens([molecule, text]))
+ return [Text.from_str(text) for text in preds]
+
+ def forward_text_based_protein_generation(self,
+ text: Featurized[Text],
+ label: Featurized[Protein],
+ ) -> Dict[str, torch.Tensor]:
+ return {"loss": self._forward(text, label)}
+
+ @torch.no_grad()
+ def predict_text_based_protein_generation(self,
+ text: Featurized[Text]
+ ) -> List[Protein]:
+ preds = self._predict(text)
+ return [self.featurizers["protein"].decode(pred) for pred in preds]
+
+ def forward_protein_question_answering(self,
+ protein: Featurized[Protein],
+ text: Featurized[Text],
+ label: Featurized[Text]
+ ) -> Dict[str, torch.Tensor]:
+ return {"loss": self._forward(concatenate_tokens([protein, text]), label)}
+
+ @torch.no_grad()
+ def predict_protein_question_answering(self,
+ protein: Featurized[Protein],
+ text: Featurized[Text]
+ ) -> List[Text]:
+ preds = self._predict(concatenate_tokens([protein, text]))
+ return [Text.from_str(text) for text in preds]
\ No newline at end of file
diff --git a/open_biomed/models/foundation_models/biot5_plus.py b/open_biomed/models/foundation_models/biot5_plus.py
new file mode 100644
index 0000000000000000000000000000000000000000..57672a192ec95791f9ede89fac9a4698b50be176
--- /dev/null
+++ b/open_biomed/models/foundation_models/biot5_plus.py
@@ -0,0 +1,145 @@
+from typing import Dict, List
+from typing_extensions import Any
+
+from huggingface_hub import snapshot_download
+import logging
+import os
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration, DataCollatorWithPadding
+from transformers.modeling_outputs import BaseModelOutput
+import selfies as sf
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.task_models import TextBasedMoleculeEditingModel, MoleculeCaptioningModel, TextGuidedMoleculeGenerationModel
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import MoleculeTransformersFeaturizer, TextTransformersFeaturizer, Featurized
+from open_biomed.utils.misc import concatenate_tokens
+
+
+class BioT5_PLUS(TextBasedMoleculeEditingModel, MoleculeCaptioningModel, TextGuidedMoleculeGenerationModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(BioT5_PLUS, self).__init__(model_cfg)
+
+ if not os.path.exists(model_cfg.hf_model_name_or_path):
+ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+ logging.info("Repo not found. Try downloading from snapshot")
+ snapshot_download(repo_id="QizhiPei/biot5-plus-base", local_dir=model_cfg.hf_model_name_or_path, force_download=True)
+ self.main_model = T5ForConditionalGeneration.from_pretrained(model_cfg.hf_model_name_or_path)
+ self.tokenizer = T5Tokenizer.from_pretrained(model_cfg.hf_model_name_or_path)
+ self.featurizers = {
+ "molecule": MoleculeTransformersFeaturizer(
+ tokenizer=self.tokenizer,
+ max_length=model_cfg.smiles_max_length,
+ base='SMILES',
+ ),
+ "text": TextTransformersFeaturizer(
+ tokenizer=self.tokenizer,
+ max_length=model_cfg.text_max_length,
+ )
+ }
+ self.collators = {
+ "molecule": DataCollatorWithPadding(self.tokenizer, padding=True),
+ "text": DataCollatorWithPadding(self.tokenizer, padding=True),
+ }
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+ def forward_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ concatenated = concatenate_tokens([molecule, text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ ) -> List[Molecule]:
+ concatenated = concatenate_tokens([molecule, text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Molecule.from_smiles(sf.decoder("".join(sel.split(" ")))) for sel in preds]
+
+ def forward_molecule_captioning(self,
+ molecule: Featurized[Molecule],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ concatenated = concatenate_tokens([molecule])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_molecule_captioning(self,
+ molecule: Featurized[Molecule],
+ ) -> List[Text]:
+ concatenated = concatenate_tokens([molecule])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Text.from_str(text) for text in preds]
+
+ def forward_text_guided_molecule_generation(self,
+ text: Featurized[Text],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ task_definition = 'Definition: You are given a molecule description. Your job is to generate the molecule SELFIES that fits the description.\n\n'
+ featurizer = self.featurizers["text"]
+ text_ori = featurizer.tokenizer.batch_decode(text["input_ids"], truncation=True, add_special_tokens=featurizer.add_special_tokens)
+ text_input = [f"{task_definition}Now complete the following example -\nInput: {i}\nOutput: " for i in text_ori]
+ text_tmp = [featurizer(Text.from_str(i)) for i in text_input]
+ collator = DataCollatorWithPadding(featurizer.tokenizer, padding=True)
+ text = collator(text_tmp)
+
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_text_guided_molecule_generation(self,
+ text: Featurized[Text],
+ ) -> List[Molecule]:
+ task_definition = 'Definition: You are given a molecule description. Your job is to generate the molecule SELFIES that fits the description.\n\n'
+ featurizer = self.featurizers["text"]
+ text_ori = featurizer.tokenizer.batch_decode(text["input_ids"], truncation=True, add_special_tokens=featurizer.add_special_tokens)
+ text_input = [f"{task_definition}Now complete the following example -\nInput: {i}\nOutput: " for i in text_ori]
+ text_tmp = [featurizer(Text.from_str(i)) for i in text_input]
+ collator = DataCollatorWithPadding(featurizer.tokenizer, padding=True)
+ text = collator(text_tmp)
+
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Molecule.from_smiles(sf.decoder("".join(sel.split(" ")))) for sel in preds]
\ No newline at end of file
diff --git a/open_biomed/models/foundation_models/molt5.py b/open_biomed/models/foundation_models/molt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..a86009c0d8c4535e2296dc9e9b877cd5f4e56b17
--- /dev/null
+++ b/open_biomed/models/foundation_models/molt5.py
@@ -0,0 +1,180 @@
+from typing import Dict, List
+from typing_extensions import Any
+
+from huggingface_hub import snapshot_download
+import logging
+import os
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration, DataCollatorWithPadding
+from transformers.modeling_outputs import BaseModelOutput
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.task_models import TextBasedMoleculeEditingModel, MoleculeCaptioningModel, TextGuidedMoleculeGenerationModel, MoleculeQAModel, ProteinQAModel
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import MoleculeTransformersFeaturizer, TextTransformersFeaturizer, Featurized
+from open_biomed.utils.misc import concatenate_tokens
+
+class MolT5(TextBasedMoleculeEditingModel, MoleculeCaptioningModel, TextGuidedMoleculeGenerationModel, MoleculeQAModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(MolT5, self).__init__(model_cfg)
+ if not os.path.exists(model_cfg.hf_model_name_or_path):
+ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+ logging.info("Repo not found. Try downloading from snapshot")
+ snapshot_download(repo_id="laituan245/molt5-base", local_dir=model_cfg.hf_model_name_or_path, force_download=True)
+ self.main_model = T5ForConditionalGeneration.from_pretrained(model_cfg.hf_model_name_or_path)
+ self.tokenizer = T5Tokenizer.from_pretrained(model_cfg.hf_model_name_or_path)
+ self.featurizers = {
+ "molecule": MoleculeTransformersFeaturizer(
+ tokenizer=self.tokenizer,
+ max_length=model_cfg.smiles_max_length,
+ base='SMILES',
+ ),
+ "text": TextTransformersFeaturizer(
+ tokenizer=self.tokenizer,
+ max_length=model_cfg.text_max_length,
+ )
+ }
+ self.collators = {
+ "molecule": DataCollatorWithPadding(self.tokenizer, padding=True),
+ "text": DataCollatorWithPadding(self.tokenizer, padding=True),
+ }
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+ def forward_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ concatenated = concatenate_tokens([molecule, text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ ) -> List[Molecule]:
+ concatenated = concatenate_tokens([molecule, text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Molecule.from_smiles(smi) for smi in preds]
+
+ def forward_molecule_captioning(self,
+ molecule: Featurized[Molecule],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ concatenated = concatenate_tokens([molecule])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_molecule_captioning(self,
+ molecule: Featurized[Molecule],
+ ) -> List[Text]:
+ concatenated = concatenate_tokens([molecule])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Text.from_str(text) for text in preds]
+
+ def forward_text_guided_molecule_generation(self,
+ text: Featurized[Text],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_text_guided_molecule_generation(self,
+ text: Featurized[Text],
+ ) -> List[Molecule]:
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Molecule.from_smiles(smi) for smi in preds]
+
+ def forward_molecule_question_answering(self,
+ text: Featurized[Text],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_molecule_question_answering(self,
+ text: Featurized[Text],
+ ) -> List[Text]:
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Text.from_str(text) for text in preds]
+
+ def forward_protein_question_answering(self,
+ text: Featurized[Text],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ return {"loss": self.main_model(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ decoder_attention_mask=label.attention_mask,
+ labels=label.input_ids
+ ).loss}
+
+ @torch.no_grad()
+ def predict_protein_question_answering(self,
+ text: Featurized[Text],
+ ) -> List[Text]:
+ concatenated = concatenate_tokens([text])
+ encoder_outputs = BaseModelOutput(last_hidden_state=self.main_model.encoder(**concatenated).last_hidden_state)
+ decoder_outputs = self.main_model.generate(
+ encoder_outputs=encoder_outputs,
+ attention_mask=concatenated.attention_mask,
+ **self.config.predict.todict(),
+ )
+ preds = self.tokenizer.batch_decode(decoder_outputs, skip_special_tokens=True)
+ return [Text.from_str(text) for text in preds]
\ No newline at end of file
diff --git a/open_biomed/models/foundation_models/pharmolix_fm.py b/open_biomed/models/foundation_models/pharmolix_fm.py
new file mode 100644
index 0000000000000000000000000000000000000000..014dcfaf96a9828923388ba7b57b288d967b079b
--- /dev/null
+++ b/open_biomed/models/foundation_models/pharmolix_fm.py
@@ -0,0 +1,1016 @@
+from typing import Any, Dict, List, Optional, Tuple
+
+import copy
+from contextlib import contextmanager
+import itertools
+import logging
+import numpy as np
+from rdkit import Chem, Geometry, RDLogger
+import re
+RDLogger.DisableLog("rdApp.*")
+import signal
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch_geometric.data import Data
+from torch_geometric.nn import knn, knn_graph
+from torch_scatter import scatter_mean, scatter_sum
+from tqdm import tqdm
+
+from open_biomed.data import Molecule, Pocket, estimate_ligand_atom_num
+from open_biomed.models.task_models import PocketMolDockModel, StructureBasedDrugDesignModel
+from open_biomed.utils.collator import PygCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import MoleculeFeaturizer, PocketFeaturizer, Featurized
+from open_biomed.utils.misc import safe_index
+
+@contextmanager
+def time_limit(seconds):
+ def signal_handler(signum, frame):
+ raise Exception("Timed out!")
+ signal.signal(signal.SIGALRM, signal_handler)
+ signal.alarm(seconds)
+ try:
+ yield
+ finally:
+ signal.alarm(0)
+
+def fix_valence(mol: Chem.RWMol) -> Tuple[Chem.RWMol, bool]:
+ # Fix valence erros in a molecule by adding electrons to N
+ mol = copy.deepcopy(mol)
+ fixed = False
+ cnt_loop = 0
+ while True:
+ try:
+ Chem.SanitizeMol(copy.deepcopy(mol))
+ fixed = True
+ Chem.SanitizeMol(mol)
+ break
+ except Chem.rdchem.AtomValenceException as e:
+ err = e
+ except Exception as e:
+ return mol, False # from HERE: rerun sample
+ cnt_loop += 1
+ if cnt_loop > 100:
+ break
+ N4_valence = re.compile(u"Explicit valence for atom # ([0-9]{1,}) N, 4, is greater than permitted")
+ index = N4_valence.findall(err.args[0])
+ if len(index) > 0:
+ mol.GetAtomWithIdx(int(index[0])).SetFormalCharge(1)
+ return mol, fixed
+
+def get_ring_sys(mol):
+ all_rings = Chem.GetSymmSSSR(mol)
+ if len(all_rings) == 0:
+ ring_sys_list = []
+ else:
+ ring_sys_list = [all_rings[0]]
+ for ring in all_rings[1:]:
+ form_prev = False
+ for prev_ring in ring_sys_list:
+ if set(ring).intersection(set(prev_ring)):
+ prev_ring.extend(ring)
+ form_prev = True
+ break
+ if not form_prev:
+ ring_sys_list.append(ring)
+ ring_sys_list = [list(set(x)) for x in ring_sys_list]
+ return ring_sys_list
+
+def get_all_subsets(ring_list):
+ all_sub_list = []
+ for n_sub in range(len(ring_list)+1):
+ all_sub_list.extend(itertools.combinations(ring_list, n_sub))
+ return all_sub_list
+
+def fix_aromatic(mol: Chem.RWMol, strict: bool=False) -> Tuple[Chem.RWMol, bool]:
+ mol_orig = mol
+ atomatic_list = [a.GetIdx() for a in mol.GetAromaticAtoms()]
+ N_ring_list = []
+ S_ring_list = []
+ for ring_sys in get_ring_sys(mol):
+ if set(ring_sys).intersection(set(atomatic_list)):
+ idx_N = [atom for atom in ring_sys if mol.GetAtomWithIdx(atom).GetSymbol() == 'N']
+ if len(idx_N) > 0:
+ idx_N.append(-1) # -1 for not add to this loop
+ N_ring_list.append(idx_N)
+ idx_S = [atom for atom in ring_sys if mol.GetAtomWithIdx(atom).GetSymbol() == 'S']
+ if len(idx_S) > 0:
+ idx_S.append(-1) # -1 for not add to this loop
+ S_ring_list.append(idx_S)
+ # enumerate S
+ fixed = False
+ if strict:
+ S_ring_list = [s for ring in S_ring_list for s in ring if s != -1]
+ permutation = get_all_subsets(S_ring_list)
+ else:
+ permutation = list(itertools.product(*S_ring_list))
+ for perm in permutation:
+ mol = copy.deepcopy(mol_orig)
+ perm = [x for x in perm if x != -1]
+ for idx in perm:
+ mol.GetAtomWithIdx(idx).SetFormalCharge(1)
+ try:
+ if strict:
+ mol, fixed = fix_valence(mol)
+ Chem.SanitizeMol(copy.deepcopy(mol))
+ fixed = True
+ Chem.SanitizeMol(mol)
+ break
+ except:
+ continue
+ # enumerate N
+ if not fixed:
+ if strict:
+ N_ring_list = [s for ring in N_ring_list for s in ring if s != -1]
+ permutation = get_all_subsets(N_ring_list)
+ else:
+ permutation = list(itertools.product(*N_ring_list))
+ for perm in permutation: # each ring select one atom
+ perm = [x for x in perm if x != -1]
+ actions = itertools.product([0, 1], repeat=len(perm))
+ for action in actions: # add H or charge
+ mol = copy.deepcopy(mol_orig)
+ for idx, act_atom in zip(perm, action):
+ if act_atom == 0:
+ mol.GetAtomWithIdx(idx).SetNumExplicitHs(1)
+ else:
+ mol.GetAtomWithIdx(idx).SetFormalCharge(1)
+ try:
+ if strict:
+ mol, fixed = fix_valence(mol)
+ Chem.SanitizeMol(copy.deepcopy(mol))
+ fixed = True
+ Chem.SanitizeMol(mol)
+ break
+ except:
+ continue
+ if fixed:
+ break
+ return mol, fixed
+
+# Featurizers
+class PharmolixFMMoleculeFeaturizer(MoleculeFeaturizer):
+ def __init__(self, pos_norm=1.0, num_node_types=12, num_edge_types=6) -> None:
+ super().__init__()
+ self.atomic_numbers = [6, 7, 8, 9, 15, 16, 17, 5, 35, 53, 34]
+ self.mol_bond_types = [
+ 'empty',
+ Chem.rdchem.BondType.SINGLE,
+ Chem.rdchem.BondType.DOUBLE,
+ Chem.rdchem.BondType.TRIPLE,
+ Chem.rdchem.BondType.AROMATIC,
+ ]
+ self.pos_norm = pos_norm
+ self.num_node_types = num_node_types
+ self.num_edge_types = num_edge_types
+
+ def __call__(self, molecule: Molecule) -> Dict[str, Any]:
+ molecule._add_rdmol()
+ rdmol = molecule.rdmol
+ node_type_list = []
+ for atom in rdmol.GetAtoms():
+ node_type_list.append(safe_index(self.atomic_numbers, atom.GetAtomicNum()))
+ node_type = F.one_hot(torch.LongTensor(node_type_list), num_classes=self.num_node_types).float()
+ num_nodes = node_type.shape[0]
+
+ if molecule.conformer is not None:
+ pos = torch.tensor(molecule.conformer).float()
+ else:
+ pos = torch.zeros(num_nodes, 3)
+ # Move to center
+ pos -= pos.mean(0)
+ pos /= self.pos_norm
+
+ # Build halfedge
+ if len(rdmol.GetBonds()) <= 0:
+ halfedge_index = torch.empty((2, 0), dtype=torch.long)
+ halfedge_type = torch.empty(0, dtype=torch.long)
+ else:
+ halfedge_matrix = torch.zeros([num_nodes, num_nodes], dtype=torch.long)
+ for bond in rdmol.GetBonds():
+ i = bond.GetBeginAtomIdx()
+ j = bond.GetEndAtomIdx()
+ bond_type = safe_index(self.mol_bond_types, bond.GetBondType())
+ halfedge_matrix[i, j] = bond_type
+ halfedge_matrix[j, i] = bond_type
+ halfedge_index = torch.triu_indices(num_nodes, num_nodes, offset=1)
+ halfedge_type = F.one_hot(halfedge_matrix[halfedge_index[0], halfedge_index[1]], num_classes=self.num_edge_types).float()
+
+ # Is peptide
+ if getattr(molecule, "is_peptide", False):
+ is_peptide = torch.ones(num_nodes, dtype=torch.long)
+ else:
+ is_peptide = torch.zeros(num_nodes, dtype=torch.long)
+
+ return Data(**{
+ "pos": pos,
+ "node_type": node_type,
+ "halfedge_type": halfedge_type,
+ "halfedge_index": halfedge_index,
+ "is_peptide": is_peptide,
+ })
+
+ def decode(self, preds: Dict[str, torch.Tensor], pocket_center: Optional[List[float]]) -> Optional[Molecule]:
+ pos = preds["pos"] * self.pos_norm
+ if pocket_center is not None:
+ pos += pocket_center
+ num_atoms = pos.shape[0]
+
+ for key in preds:
+ preds[key] = preds[key].cpu().numpy()
+
+ # Add atoms and coordinates
+ rdmol = Chem.RWMol()
+ conf = Chem.Conformer()
+ for i in range(num_atoms):
+ atom = Chem.Atom(self.atomic_numbers[preds["node_type"][i]])
+ rdmol.AddAtom(atom)
+ atom_pos = Geometry.Point3D(*pos[i].tolist())
+ conf.SetAtomPosition(i, atom_pos)
+ rdmol.AddConformer(conf)
+
+ # Add bonds
+ bond_index = torch.triu_indices(num_atoms, num_atoms, offset=1).numpy()
+ for i in range(bond_index.shape[1]):
+ st, ed = bond_index[0][i], bond_index[1][i]
+ if preds["halfedge_type"][i] > 0:
+ rdmol.AddBond(int(st), int(ed), self.mol_bond_types[preds["halfedge_type"][i]])
+
+ # Check validity and fix N valence
+ mol = rdmol.GetMol()
+ try:
+ Chem.SanitizeMol(copy.deepcopy(mol))
+ fixed = True
+ Chem.SanitizeMol(mol)
+ except Exception as e:
+ fixed = False
+
+ if not fixed:
+ try:
+ Chem.Kekulize(copy.deepcopy(mol))
+ except Chem.rdchem.KekulizeException as e:
+ err = e
+ if 'Unkekulized' in err.args[0]:
+ try:
+ with time_limit(300):
+ mol, fixed = fix_aromatic(mol)
+ except Exception as e:
+ logging.warn('Timeout for fixing aromatic rings')
+ return None
+
+ # valence error for N
+ if not fixed:
+ try:
+ with time_limit(300):
+ mol, fixed = fix_valence(mol)
+ except Exception as e:
+ logging.warn('Timeout for fixing valence')
+ return None
+
+ if not fixed:
+ try:
+ with time_limit(300):
+ mol, fixed = fix_aromatic(mol, True)
+ except Exception as e:
+ logging.warn('Timeout for fixing aromatic rings')
+ return None
+
+ try:
+ Chem.SanitizeMol(mol)
+ except Exception as e:
+ logging.warn('Failed generate a valid molecule')
+ return None
+ return Molecule.from_rdmol(mol)
+
+class PharmolixFMPocketFeaturizer(PocketFeaturizer):
+ def __init__(self, knn: int=32, pos_norm: float=1.0) -> None:
+ super().__init__()
+
+ self.knn = knn
+ self.pos_norm = pos_norm
+ self.atomic_numbers = torch.LongTensor([6, 7, 8, 16]) # C, N, O, S
+ self.max_num_aa = 20
+
+ def __call__(self, pocket: Pocket) -> Dict[str, Any]:
+ elements = torch.LongTensor([atom["atomic_number"] for atom in pocket.atoms])
+ elements_one_hot = (elements.view(-1, 1) == self.atomic_numbers.view(1, -1)).long()
+ aa_type = torch.LongTensor([atom["aa_type"] for atom in pocket.atoms])
+ aa_one_hot = F.one_hot(aa_type, num_classes=self.max_num_aa)
+ is_backbone = torch.LongTensor([atom["is_backbone"] for atom in pocket.atoms]).unsqueeze(-1)
+
+ x = torch.cat([elements_one_hot, aa_one_hot, is_backbone], dim=-1).float()
+ pos = torch.tensor(pocket.conformer, dtype=torch.float32)
+ knn_edge_index = knn_graph(pos, k=self.knn, flow='target_to_source')
+ pocket_center = pos.mean(dim=0)
+ pos -= pocket_center
+ pos /= self.pos_norm
+
+ return Data(**{
+ "atom_feature": x,
+ "knn_edge_index": knn_edge_index,
+ "pos": pos,
+ "pocket_center": pocket_center.unsqueeze(0),
+ "estimated_ligand_num_atoms": torch.tensor(estimate_ligand_atom_num(pocket)).unsqueeze(0),
+ })
+
+# Model Layers
+class MLP(nn.Module):
+ """MLP with the same hidden dim across all layers."""
+ NONLINEARITIES = {
+ "tanh": nn.Tanh(),
+ "relu": nn.ReLU(),
+ "softplus": nn.Softplus(),
+ "elu": nn.ELU(),
+ 'silu': nn.SiLU()
+ }
+
+ def __init__(self, in_dim, out_dim, hidden_dim, num_layer=2, norm=True, act_fn='relu', act_last=False):
+ super().__init__()
+ layers = []
+ for layer_idx in range(num_layer):
+ if layer_idx == 0:
+ layers.append(nn.Linear(in_dim, hidden_dim))
+ elif layer_idx == num_layer - 1:
+ layers.append(nn.Linear(hidden_dim, out_dim))
+ else:
+ layers.append(nn.Linear(hidden_dim, hidden_dim))
+ if layer_idx < num_layer - 1 or act_last:
+ if norm:
+ layers.append(nn.LayerNorm(hidden_dim))
+ layers.append(self.NONLINEARITIES[act_fn])
+ self.net = nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.net(x)
+
+class GaussianSmearing(nn.Module):
+ def __init__(self, start=0.0, stop=10.0, num_gaussians=50, type_='exp'):
+ super().__init__()
+ self.start = start
+ self.stop = stop
+ if type_ == 'exp':
+ offset = torch.exp(torch.linspace(start=np.log(start+1), end=np.log(stop+1), steps=num_gaussians)) - 1
+ elif type_ == 'linear':
+ offset = torch.linspace(start=start, end=stop, steps=num_gaussians)
+ else:
+ raise NotImplementedError('type_ must be either exp or linear')
+ diff = torch.diff(offset)
+ diff = torch.cat([diff[:1], diff])
+ coeff = -0.5 / (diff**2)
+ self.register_buffer('coeff', coeff)
+ self.register_buffer('offset', offset)
+
+ def forward(self, dist):
+ dist = dist.clamp_min(self.start)
+ dist = dist.clamp_max(self.stop)
+ dist = dist.view(-1, 1) - self.offset.view(1, -1)
+ return torch.exp(self.coeff * torch.pow(dist, 2))
+
+class ContextNodeBlock(nn.Module):
+ def __init__(self, node_dim, edge_dim, hidden_dim, gate_dim,
+ context_dim=0, context_edge_dim=0, layernorm_before=False):
+ super().__init__()
+ self.node_dim = node_dim
+ self.edge_dim = edge_dim
+ self.gate_dim = gate_dim
+ self.context_dim = context_dim
+ self.context_edge_dim = context_edge_dim
+ self.layernorm_before = layernorm_before
+
+ self.node_net = MLP(node_dim, hidden_dim, hidden_dim)
+ self.edge_net = MLP(edge_dim, hidden_dim, hidden_dim)
+ self.msg_net = nn.Linear(hidden_dim, hidden_dim)
+
+ if self.gate_dim > 0:
+ self.gate = MLP(edge_dim+(node_dim+gate_dim)*2, hidden_dim, hidden_dim)
+
+ self.centroid_lin = nn.Linear(node_dim, hidden_dim)
+ self.layer_norm = nn.LayerNorm(hidden_dim)
+ # self.act = nn.ReLU()
+ # self.out_transform = Linear(hidden_dim, node_dim)
+ self.out_layer = MLP(hidden_dim, node_dim, hidden_dim)
+
+ if self.context_dim > 0:
+ self.ctx_node_net = MLP(context_dim, hidden_dim, hidden_dim)
+ self.ctx_edge_net = MLP(context_edge_dim, hidden_dim, hidden_dim)
+ self.ctx_msg_net = nn.Linear(hidden_dim, hidden_dim)
+ self.ctx_gate = MLP(context_dim+context_edge_dim+(node_dim+gate_dim), hidden_dim, hidden_dim)
+
+ def forward(self, x, edge_index, edge_attr, node_extra,
+ ctx_x=None, ctx_edge_index=None, ctx_edge_attr=None):
+ """
+ Args:
+ x: Node features, (N, H).
+ edge_index: (2, E).
+ edge_attr: (E, H)
+ """
+ N = x.size(0)
+ row, col = edge_index # (E,) , (E,)
+
+ h_node = self.node_net(x) # (N, H)
+
+ # Compose messages
+ h_edge = self.edge_net(edge_attr) # (E, H_per_head)
+ msg_j = self.msg_net(h_edge + h_node[col] + h_node[row])
+
+ if self.gate_dim > 0:
+ gate = self.gate(torch.cat([edge_attr, x[col], node_extra[col], x[row], node_extra[row]], dim=-1))
+ msg_j = msg_j * torch.sigmoid(gate)
+
+ # Aggregate messages
+ aggr_msg = scatter_sum(msg_j, row, dim=0, dim_size=N)
+ out = self.centroid_lin(x) + aggr_msg
+
+ # context messages
+ if ctx_x is not None:
+ row, col = ctx_edge_index
+ h_ctx = self.ctx_node_net(ctx_x)
+ h_ctx_edge = self.ctx_edge_net(ctx_edge_attr)
+ msg_ctx = self.ctx_msg_net(h_ctx_edge * h_ctx[col])
+ if self.gate_dim > 0:
+ gate = self.ctx_gate(torch.cat([ctx_edge_attr, ctx_x[col], x[row], node_extra[row]], dim=-1))
+ msg_ctx = msg_ctx * torch.sigmoid(gate)
+ aggred_ctx_msg = scatter_sum(msg_ctx, row, dim=0, dim_size=N)
+ out = out + aggred_ctx_msg
+
+ # output. skip connection
+ out = self.out_layer(out)
+ if not self.layernorm_before:
+ out = self.layer_norm(out + x)
+ else:
+ out = self.layer_norm(out) + x
+ return out
+
+class BondFFN(nn.Module):
+ def __init__(self, bond_dim, node_dim, inter_dim, gate_dim, out_dim=None):
+ super().__init__()
+ out_dim = bond_dim if out_dim is None else out_dim
+ self.gate_dim = gate_dim
+ self.bond_linear = nn.Linear(bond_dim, inter_dim, bias=False)
+ self.node_linear = nn.Linear(node_dim, inter_dim, bias=False)
+ self.inter_module = MLP(inter_dim, out_dim, inter_dim)
+ if self.gate_dim > 0:
+ self.gate = MLP(bond_dim+node_dim+gate_dim, out_dim, 32)
+
+ def forward(self, bond_feat_input, node_feat_input, extra):
+ bond_feat = self.bond_linear(bond_feat_input)
+ node_feat = self.node_linear(node_feat_input)
+ inter_feat = bond_feat + node_feat
+ inter_feat = self.inter_module(inter_feat)
+ if self.gate_dim > 0:
+ gate = self.gate(torch.cat([bond_feat_input, node_feat_input, extra], dim=-1))
+ inter_feat = inter_feat * torch.sigmoid(gate)
+ return inter_feat
+
+
+class EdgeBlock(nn.Module):
+ def __init__(self, edge_dim, node_dim, hidden_dim=None, gate_dim=0, layernorm_before=False):
+ super().__init__()
+ self.gate_dim = gate_dim
+ inter_dim = edge_dim * 2 if hidden_dim is None else hidden_dim
+ self.layernorm_before = layernorm_before
+
+ self.bond_ffn_left = BondFFN(edge_dim, node_dim, inter_dim=inter_dim, gate_dim=gate_dim)
+ self.bond_ffn_right = BondFFN(edge_dim, node_dim, inter_dim=inter_dim, gate_dim=gate_dim)
+
+ self.msg_left = nn.Linear(edge_dim, edge_dim)
+ self.msg_right = nn.Linear(edge_dim, edge_dim)
+
+ self.node_ffn_left = nn.Linear(node_dim, edge_dim)
+ self.node_ffn_right = nn.Linear(node_dim, edge_dim)
+
+ self.self_ffn = nn.Linear(edge_dim, edge_dim)
+ self.layer_norm = nn.LayerNorm(edge_dim)
+ self.out_layer = MLP(edge_dim, edge_dim, edge_dim)
+
+ def forward(self, h_bond, bond_index, h_node, bond_extra):
+ """
+ h_bond: (b, bond_dim)
+ bond_index: (2, b)
+ h_node: (n, node_dim)
+ """
+ N = h_node.size(0)
+ left_node, right_node = bond_index
+
+ # message from neighbor bonds
+ msg_bond_left = self.bond_ffn_left(h_bond, h_node[left_node], bond_extra)
+ msg_bond_left = scatter_sum(msg_bond_left, right_node, dim=0, dim_size=N)
+ msg_bond_left = msg_bond_left[left_node]
+
+ msg_bond_right = self.bond_ffn_right(h_bond, h_node[right_node], bond_extra)
+ msg_bond_right = scatter_sum(msg_bond_right, left_node, dim=0, dim_size=N)
+ msg_bond_right = msg_bond_right[right_node]
+
+ h_bond_update = (
+ self.msg_left(msg_bond_left)
+ + self.msg_right(msg_bond_right)
+ + self.node_ffn_left(h_node[left_node])
+ + self.node_ffn_right(h_node[right_node])
+ + self.self_ffn(h_bond)
+ )
+ h_bond_update = self.out_layer(h_bond_update)
+
+ # skip connection
+ if not self.layernorm_before:
+ h_bond = self.layer_norm(h_bond_update + h_bond)
+ else:
+ h_bond = self.layer_norm(h_bond_update) + h_bond
+ return h_bond
+
+class PosUpdate(nn.Module):
+ def __init__(self, node_dim, edge_dim, hidden_dim, gate_dim, node_dim_right=None):
+ super().__init__()
+ self.left_lin_edge = MLP(node_dim, node_dim, hidden_dim)
+ node_dim_right = node_dim if node_dim_right is None else node_dim_right
+ self.right_lin_edge = MLP(node_dim_right, node_dim, hidden_dim)
+ self.edge_lin = BondFFN(edge_dim, node_dim*2, node_dim, gate_dim, out_dim=1)
+ self.pos_scale_net = nn.Sequential(MLP(node_dim+1+2, 1, hidden_dim), nn.Sigmoid())
+
+ def forward(self, h_node, h_edge, edge_index, relative_vec, distance, node_extra, edge_extra=None, h_node_right=None):
+ edge_index_left, edge_index_right = edge_index
+
+ left_feat = self.left_lin_edge(h_node[edge_index_left])
+ h_node_right = h_node if h_node_right is None else h_node_right
+ right_feat = self.right_lin_edge(h_node_right[edge_index_right])
+ both_extra = node_extra[edge_index_left]
+ if edge_extra is not None:
+ both_extra = torch.cat([both_extra, edge_extra], dim=-1)
+ weight_edge = self.edge_lin(h_edge,
+ torch.cat([left_feat, right_feat], dim=-1),
+ both_extra)
+
+ force_edge = weight_edge * relative_vec / (distance.unsqueeze(-1) + 1e-6) / (distance.unsqueeze(-1) + 5.) * 5
+ delta_pos = scatter_sum(force_edge, edge_index_left, dim=0, dim_size=h_node.shape[0])
+ delta_pos = delta_pos * self.pos_scale_net(torch.cat([h_node, node_extra,
+ torch.norm(delta_pos, dim=-1, keepdim=True)], dim=-1))
+ return delta_pos
+
+class ContextNodeEdgeNet(nn.Module):
+ def __init__(self, node_dim, edge_dim, hidden_dim,
+ num_blocks, dist_cfg, gate_dim=0,
+ context_dim=0, context_cfg=None,
+ node_only=False, **kwargs):
+ super().__init__()
+ self.node_dim = node_dim
+ self.edge_dim = edge_dim
+ self.num_blocks = num_blocks
+ self.dist_cfg = dist_cfg
+ self.gate_dim = gate_dim
+ self.node_only = node_only
+ self.kwargs = kwargs
+ self.downsample_context = kwargs.get('downsample_context', False)
+ self.layernorm_before = kwargs.get("layernorm_before", False)
+
+ self.distance_expansion = GaussianSmearing(**dist_cfg)
+ num_gaussians = dist_cfg['num_gaussians']
+ input_edge_dim = num_gaussians + (0 if node_only else edge_dim)
+
+ # for context
+ self.context_cfg = context_cfg
+ if context_cfg is not None:
+ context_edge_dim = context_cfg['edge_dim']
+ self.knn = context_cfg['knn']
+ self.dist_exp_ctx = GaussianSmearing(**context_cfg['dist_cfg'])
+ input_context_edge_dim = context_cfg['dist_cfg']['num_gaussians']
+ assert context_dim > 0, 'context_dim should be larger than 0 if context_cfg is not None'
+ assert not node_only, 'not support node_only with context'
+ else:
+ context_edge_dim = 0
+
+ # node network
+ self.edge_embs = nn.ModuleList()
+ self.node_blocks_with_edge = nn.ModuleList()
+ if not node_only:
+ self.edge_blocks = nn.ModuleList()
+ self.pos_blocks = nn.ModuleList()
+ if self.context_cfg is not None:
+ self.ctx_edge_embs = nn.ModuleList()
+ self.ctx_pos_blocks = nn.ModuleList()
+ for _ in range(num_blocks):
+ # edge emb
+ self.edge_embs.append(nn.Linear(input_edge_dim, edge_dim))
+ # node update
+ self.node_blocks_with_edge.append(ContextNodeBlock(
+ node_dim, edge_dim, hidden_dim, gate_dim,
+ context_dim, context_edge_dim, layernorm_before=self.layernorm_before
+ ))
+ if node_only:
+ continue
+ # edge update
+ self.edge_blocks.append(EdgeBlock(
+ edge_dim=edge_dim, node_dim=node_dim, gate_dim=gate_dim, layernorm_before=self.layernorm_before
+ ))
+ # pos update
+ self.pos_blocks.append(PosUpdate(
+ node_dim, edge_dim, hidden_dim=edge_dim, gate_dim=gate_dim*2,
+ ))
+ if self.context_cfg is not None:
+ self.ctx_edge_embs.append(nn.Linear(input_context_edge_dim, context_edge_dim))
+ self.ctx_pos_blocks.append(PosUpdate(
+ node_dim, context_edge_dim, hidden_dim=edge_dim, gate_dim=gate_dim,
+ node_dim_right=context_dim,
+ ))
+
+ def forward(self, h_node, pos_node, h_edge, edge_index,
+ node_extra, edge_extra, batch_node=None,
+ h_ctx=None, pos_ctx=None, batch_ctx=None):
+ """
+ graph node/edge features
+ h_node: (n_node, node_dim)
+ pos_node: (n_node, 3)
+ h_edge: (n_edge, edge_dim)
+ edge_index: (2, n_edge)
+ node_extra: (n_node, node_extra_dim)
+ edge_extra: (n_edge, edge_extra_dim)
+ batch_node: (n_node, )
+ context node features
+ h_ctx: (n_ctx, ctx_dim)
+ pos_ctx: (n_ctx, 3)
+ batch_ctx: (n_ctx, )
+ Output:
+ h_node: (n_node, node_dim)
+ h_edge: (n_edge, edge_dim)
+ pos_node: (n_node, 3)
+ """
+
+ for i in range(self.num_blocks):
+ # # remake edge fetures (distance have been changed in each iteration)
+ if (i==0) or (not self.node_only):
+ h_dist, relative_vec, distance = self._build_edges_dist(pos_node, edge_index)
+ if not self.node_only:
+ h_edge = torch.cat([h_edge, h_dist], dim=-1)
+ else:
+ h_edge = h_dist
+ h_edge = self.edge_embs[i](h_edge)
+
+ # # edge with context
+ if h_ctx is not None:
+ h_ctx_edge, vec_ctx, dist_ctx, ctx_knn_edge_index = self._build_context_edges_dist(
+ pos_node, pos_ctx, batch_node, batch_ctx)
+ h_ctx_edge = self.ctx_edge_embs[i](h_ctx_edge)
+ else:
+ ctx_knn_edge_index = None
+ h_ctx_edge = None
+
+ # # node feature updates
+ h_node = self.node_blocks_with_edge[i](h_node, edge_index, h_edge, node_extra,
+ h_ctx, ctx_knn_edge_index, h_ctx_edge)
+ if self.node_only:
+ continue
+
+ # # edge feature updates
+ h_edge = self.edge_blocks[i](h_edge, edge_index, h_node, edge_extra)
+
+ # # pos updates
+ pos_node = pos_node + self.pos_blocks[i](h_node, h_edge, edge_index, relative_vec, distance, node_extra, edge_extra)
+ if h_ctx is not None:
+ pos_node = pos_node + self.ctx_pos_blocks[i](
+ h_node, h_ctx_edge, ctx_knn_edge_index, vec_ctx, dist_ctx, node_extra,
+ edge_extra=None, h_node_right=h_ctx)
+
+ if self.node_only:
+ return h_node
+ else:
+ return h_node, pos_node, h_edge
+
+ def _build_edges_dist(self, pos, edge_index):
+ # distance
+ relative_vec = pos[edge_index[0]] - pos[edge_index[1]]
+ distance = torch.norm(relative_vec, dim=-1, p=2)
+ h_dist = self.distance_expansion(distance)
+ return h_dist, relative_vec, distance
+
+ def _build_context_edges_dist(self, pos, pos_ctx, batch_node, batch_ctx):
+ # build knn edge index
+ if self.knn < 100:
+ if self.downsample_context:
+ pos_ctx_noised = pos_ctx + torch.randn_like(pos_ctx) * 5 # works like masked position information
+ else:
+ pos_ctx_noised = pos_ctx
+ ctx_knn_edge_index = knn(y=pos, x=pos_ctx_noised, k=self.knn,
+ batch_x=batch_ctx, batch_y=batch_node)
+ else: # fully connected x-yf
+ device = pos.device
+ ctx_knn_edge_index = []
+ cum_node = 0
+ cum_ctx = 0
+ for i_batch in range(batch_ctx.max()+1):
+ num_ctx = (batch_ctx==i_batch).sum()
+ num_node = (batch_node==i_batch).sum()
+ ctx_knn_edge_index_this = torch.stack(
+ torch.meshgrid(
+ torch.arange(num_node, device=device) + cum_node,
+ torch.arange(num_ctx, device=device) + cum_ctx,
+ )).view(2, -1)
+ cum_node += num_node
+ cum_ctx += num_ctx
+ ctx_knn_edge_index.append(ctx_knn_edge_index_this)
+ ctx_knn_edge_index = torch.cat(ctx_knn_edge_index, dim=-1)
+
+ relative_vec = pos[ctx_knn_edge_index[0]] - pos_ctx[ctx_knn_edge_index[1]]
+ distance = torch.norm(relative_vec, dim=-1, p=2)
+ h_dist = self.dist_exp_ctx(distance)
+ return h_dist, relative_vec, distance, ctx_knn_edge_index
+
+class PharmolixFM(PocketMolDockModel, StructureBasedDrugDesignModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(PharmolixFM, self).__init__(model_cfg)
+ self.config = model_cfg
+ self.num_node_types = model_cfg.num_node_types
+ self.num_edge_types = model_cfg.num_edge_types
+
+ # # pocket encoder
+ pocket_dim = model_cfg.pocket_dim
+ self.pocket_embedder = nn.Linear(model_cfg.pocket_in_dim, pocket_dim)
+ self.pocket_encoder = ContextNodeEdgeNet(pocket_dim, node_only=True, **model_cfg.pocket.todict())
+
+ # # mol embedding
+ self.addition_node_features = getattr(model_cfg, 'addition_node_features', [])
+ node_dim = model_cfg.node_dim
+ edge_dim = model_cfg.edge_dim
+ node_emb_dim = node_dim - len(self.addition_node_features)
+ edge_emb_dim = edge_dim
+ self.nodetype_embedder = nn.Linear(self.num_node_types + 2, node_emb_dim) # t_pos_in and t_node_in
+ self.edgetype_embedder = nn.Linear(self.num_edge_types + 2, edge_emb_dim) # t_halfedge_in and fixed_halfdist
+
+ # # bfn backbone
+ self.denoiser = ContextNodeEdgeNet(node_dim, edge_dim,
+ context_dim=pocket_dim, **model_cfg.denoiser.todict())
+
+ # # decoder for discrete variables
+ self.node_decoder = MLP(node_dim, self.num_node_types, node_dim)
+ self.edge_decoder = MLP(edge_dim, self.num_edge_types, edge_dim)
+
+ # additional output
+ self.add_output = getattr(model_cfg, 'add_output', [])
+ if 'confidence' in self.add_output: # condidence
+ self.node_cfd = MLP(node_dim, 1, node_dim // 2)
+ self.pos_cfd = MLP(node_dim, 1, node_dim // 2)
+ self.edge_cfd = MLP(edge_dim, 1, edge_dim // 2)
+
+ self.featurizers = {
+ "molecule": PharmolixFMMoleculeFeaturizer(model_cfg.pos_norm),
+ "pocket": PharmolixFMPocketFeaturizer(model_cfg.pocket_knn, model_cfg.pos_norm),
+ }
+ self.collators = {
+ "molecule": PygCollator(follow_batch=["pos", "node_type", "halfedge_type"]),
+ "pocket": PygCollator(follow_batch=["pos"])
+ }
+
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+ def continuous_var_bayesian_update(self, t: torch.Tensor, x: torch.Tensor, fixed_pos: torch.Tensor=None, orig_x: torch.Tensor=None) -> torch.Tensor:
+ # Eq.(77): p_F(θ|x;t) ~ N (μ | γ(t)x, γ(t)(1 − γ(t))I)
+ gamma = (1 - torch.pow(self.config.sigma1, 2 * t)).unsqueeze(1) # [B]
+ mu = gamma * x + torch.sqrt((gamma + 0.01) * (1 - gamma)) * torch.randn_like(x)
+ if fixed_pos is not None:
+ mu[torch.where(fixed_pos)] = orig_x[torch.where(fixed_pos)]
+ return mu, gamma
+
+ def discrete_var_bayesian_update(self, t: torch.Tensor, x: torch.Tensor, K: int, fixed_pos: torch.Tensor=None, orig_x: torch.Tensor=None) -> torch.Tensor:
+ # Eq.(182): β(t) = t**2 β(1)
+ beta = (self.config.beta1 * (t**2)).unsqueeze(1) # (B,)
+
+ # Eq.(185): p_F(θ|x;t) = E_{N(y | β(t)(Ke_x−1), β(t)KI)} δ (θ − softmax(y))
+ # can be sampled by first drawing y ~ N(y | β(t)(Ke_x−1), β(t)KI)
+ # then setting θ = softmax(y)
+ one_hot_x = x # (N, K)
+ mean = beta * (K * one_hot_x - 1)
+ std = (beta * K).sqrt()
+ eps = torch.randn_like(mean)
+ y = mean + std * eps
+ theta = F.softmax(y, dim=-1)
+ if fixed_pos is not None:
+ theta[torch.where(fixed_pos)] = orig_x[torch.where(fixed_pos)]
+ return theta
+
+ def create_dummy_molecule(self, pocket: Featurized[Pocket]) -> Featurized[Molecule]:
+ num_atoms = pocket["estimated_ligand_num_atoms"].cpu()
+ num_halfedge = (num_atoms ** 2 - num_atoms) // 2
+ batch_size = num_atoms.shape[0]
+ halfedge_index = []
+ cur_num = 0
+ for num in num_atoms:
+ halfedge_index.append(torch.triu_indices(num, num, offset=1) + cur_num)
+ cur_num += num
+
+ return Data(**{
+ "pos": torch.randn(num_atoms.sum().item(), 3) * 0.01,
+ "node_type": torch.ones(num_atoms.sum().item(), self.num_node_types) / self.num_node_types,
+ "halfedge_type": torch.ones(num_halfedge.sum().item(), self.num_edge_types) / self.num_edge_types,
+ "is_peptide": torch.zeros(num_atoms.sum().item(), dtype=torch.long),
+ "halfedge_index": torch.cat(halfedge_index, dim=1),
+ "pos_batch": torch.repeat_interleave(torch.arange(batch_size), num_atoms),
+ "node_type_batch": torch.repeat_interleave(torch.arange(batch_size), num_atoms),
+ "halfedge_type_batch": torch.repeat_interleave(torch.arange(batch_size), num_halfedge),
+ }).to(pocket["atom_feature"].device)
+
+ def model_forward(self,
+ molecule: Featurized[Molecule],
+ pocket: Optional[Featurized[Pocket]],
+ ) -> Dict[str, torch.Tensor]:
+ pos_in = molecule['pos']
+
+ if len(molecule['node_type'].shape) <= 1:
+ molecule['node_type'] = F.one_hot(molecule['node_type'], self.num_node_types).float()
+ molecule['halfedge_type'] = F.one_hot(molecule['halfedge_type'], self.num_edge_types).float()
+ if 't_node_type' not in molecule:
+ molecule['t_node_type'] = torch.ones(molecule['node_type'].shape[0], dtype=torch.float, device=molecule['node_type'].device)
+ molecule['t_halfedge_type'] = torch.ones(molecule['halfedge_type'].shape[0], dtype=torch.float, device=molecule['halfedge_type'].device)
+
+ h_node_in = molecule['node_type']
+ h_halfedge_in = molecule['halfedge_type']
+
+ # add t indicator as extra features
+ node_extra = torch.stack([molecule['t_node_type'], molecule['t_pos']], dim=1).to(pos_in.dtype)
+ halfedge_extra = torch.stack([molecule['t_halfedge_type'], molecule['fixed_halfdist']], dim=1).to(pos_in.dtype)
+ h_node_in = torch.cat([h_node_in, node_extra], dim=-1)
+ h_halfedge_in = torch.cat([h_halfedge_in, halfedge_extra], dim=-1)
+
+ # from 1/K \in [0,1] to 2/K-1 \in [-1,1]
+ h_node_in = self.nodetype_embedder(2 * h_node_in - 1)
+ h_halfedge_in = self.edgetype_embedder(2 * h_halfedge_in - 1)
+
+ # break symmetry
+ n_halfedges = h_halfedge_in.shape[0]
+ halfedge_index = molecule['halfedge_index']
+ edge_index = torch.cat([halfedge_index, halfedge_index.flip(0)], dim=1)
+ h_edge_in = torch.cat([h_halfedge_in, h_halfedge_in], dim=0)
+ edge_extra = torch.cat([halfedge_extra, halfedge_extra], dim=0)
+
+ # additonal node features
+ if 'is_peptide' in self.addition_node_features:
+ is_peptide = molecule['is_peptide'].unsqueeze(-1).to(pos_in.dtype)
+ h_node_in = torch.cat([h_node_in, is_peptide], dim=-1)
+
+ # # encode pocket
+ h_pocket = self.pocket_embedder(pocket['atom_feature'])
+ h_pocket = self.pocket_encoder(
+ h_node=h_pocket,
+ pos_node=pocket['pos'],
+ edge_index=pocket['knn_edge_index'],
+ h_edge=None,
+ node_extra=None,
+ edge_extra=None,
+ )
+
+ # # 2 interdependency modeling
+ h_node, pred_pos, h_edge = self.denoiser(
+ h_node=h_node_in,
+ pos_node=pos_in,
+ h_edge=h_edge_in,
+ edge_index=edge_index,
+ node_extra=node_extra,
+ edge_extra=edge_extra,
+ batch_node=molecule['node_type_batch'],
+ # pocket
+ h_ctx=h_pocket,
+ pos_ctx=pocket['pos'],
+ batch_ctx=pocket['pos_batch'],
+ )
+
+ # # 3 predict the variables
+ # for discrete, we take softmax
+ node_logits = self.node_decoder(h_node)
+ pred_node = F.softmax(node_logits, dim=-1)
+ halfedge_logits = self.edge_decoder(h_edge[:n_halfedges] + h_edge[n_halfedges:])
+ pred_halfedge = F.softmax(halfedge_logits, dim=-1)
+
+ additional_outputs = {}
+ if 'confidence' in self.add_output:
+ pred_node_cfd = self.node_cfd(h_node)
+ pred_pos_cfd = self.pos_cfd(h_node) # use the node hidden
+ pred_edge_cfd = self.edge_cfd(h_edge[:n_halfedges]+h_edge[n_halfedges:]) # NOTE why not divide by 2?
+ additional_outputs = {'confidence_node_type': pred_node_cfd, 'confidence_pos': pred_pos_cfd, 'confidence_halfedge_type': pred_edge_cfd}
+
+ return {
+ 'pos': pred_pos,
+ 'node_type': pred_node,
+ 'halfedge_type': pred_halfedge,
+ **additional_outputs,
+ }
+
+ @torch.no_grad()
+ def sample(self,
+ molecule: Featurized[Molecule],
+ pocket: Optional[Featurized[Pocket]]=None,
+ ) -> List[Molecule]:
+ # Initialization
+ device = molecule['pos'].device
+
+ molecule_in = {
+ "pos": torch.randn_like(molecule['pos']) * 0.01,
+ "node_type": torch.ones_like(molecule['node_type']) / self.config.num_node_types,
+ "halfedge_type": torch.ones_like(molecule['halfedge_type']) / self.config.num_edge_types,
+ }
+ in_traj, out_traj, cfd_traj = {}, {}, {}
+ for key in molecule_in:
+ fixed = molecule[f"fixed_{key}"]
+ if fixed is not None:
+ molecule_in[key][fixed] = molecule[key][fixed]
+ molecule[key] = molecule_in[key]
+ in_traj[key] = []
+ out_traj[key] = []
+ cfd_traj[key] = []
+
+ # BFN update
+ for step in tqdm(range(1, self.config.num_sample_steps + 1), desc="Sampling"):
+ t = torch.ones(1, dtype=torch.float, device=device) * (step - 1) / self.config.num_sample_steps
+ t_in = {
+ "pos": t.repeat(molecule["pos"].shape[0]),
+ "node_type": t.repeat(molecule["pos"].shape[0]),
+ "halfedge_type": t.repeat(molecule["halfedge_type"].shape[0]),
+ }
+ for key in t_in:
+ fixed = molecule[f"fixed_{key}"]
+ t_in[key][torch.where(fixed)] = 1
+ molecule[f"t_{key}"] = t_in[key]
+ outputs_step = self.model_forward(molecule, pocket)
+ for key in t_in:
+ in_traj[key].append(copy.deepcopy(molecule[key]))
+ out_traj[key].append(copy.deepcopy(outputs_step[key]))
+ cfd_traj[key].append(copy.deepcopy(outputs_step[f"confidence_{key}"]))
+
+ # Destination prediction
+ if step == self.config.num_sample_steps:
+ for key in t_in:
+ molecule[key] = outputs_step[key]
+ continue
+
+ molecule["pos"], _ = self.continuous_var_bayesian_update(
+ t_in["pos"], outputs_step["pos"],
+ fixed_pos=molecule["fixed_pos"], orig_x=molecule["pos"]
+ )
+ molecule["node_type"] = self.discrete_var_bayesian_update(
+ t_in["node_type"], outputs_step["node_type"], self.num_node_types,
+ fixed_pos=molecule["fixed_node_type"], orig_x=molecule["node_type"]
+ )
+ molecule["halfedge_type"] = self.discrete_var_bayesian_update(
+ t_in["halfedge_type"], outputs_step["halfedge_type"], self.num_edge_types,
+ fixed_pos=molecule["fixed_halfedge_type"], orig_x=molecule["halfedge_type"]
+ )
+
+ # Concat trajectories
+ for key in molecule_in:
+ in_traj[key] = torch.stack(in_traj[key], dim=0)
+ out_traj[key] = torch.stack(out_traj[key], dim=0)
+ cfd_traj[key] = torch.stack(cfd_traj[key], dim=0)
+
+ # Split and reconstruct molecule
+ num_mols = molecule["node_type_batch"].max() + 1
+ in_traj_split, out_traj_split, cfd_traj_split = [], [], []
+ out_molecules = []
+ for i in tqdm(range(num_mols), desc="Post processing molecules..."):
+ in_traj_split.append({})
+ out_traj_split.append({})
+ cfd_traj_split.append({})
+ cur_molecule = {}
+ for key in molecule_in:
+ idx = torch.where(molecule[f"{key}_batch"] == i)[0]
+ in_traj_split[i][key] = in_traj[key][:, idx, :]
+ out_traj_split[i][key] = out_traj[key][:, idx, :]
+ cfd_traj_split[i][key] = cfd_traj[key][:, idx, :]
+ cur_molecule[key] = out_traj_split[i][key][-1]
+ cur_molecule["node_type"] = torch.argmax(cur_molecule["node_type"], dim=-1)
+ cur_molecule["halfedge_type"] = torch.argmax(cur_molecule["halfedge_type"], dim=-1)
+ out_molecules.append(self.featurizers["molecule"].decode(cur_molecule, pocket["pocket_center"][i]))
+ """
+ import pickle
+ from datetime import datetime
+ pickle.dump({
+ "in_traj": in_traj_split[0],
+ "out_traj": out_traj_split[0],
+ "cfd_traj": cfd_traj_split[0],
+ }, open(f"./tmp/debug_traj_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pkl", "wb"))
+ """
+ return out_molecules
+
+ # TODO: implement training of PharMolixFM
+ def forward_pocket_molecule_docking(self, pocket: Featurized[Pocket], label: Featurized[Molecule]) -> Dict[str, torch.Tensor]:
+ pass
+
+ def forward_structure_based_drug_design(self, pocket: List[Pocket], label: List[Molecule]) -> Dict[str, torch.Tensor]:
+ pass
+
+ def predict_pocket_molecule_docking(self,
+ molecule: Featurized[Molecule],
+ pocket: Featurized[Pocket],
+ ) -> List[Molecule]:
+ molecule["fixed_pos"] = torch.zeros(molecule["pos"].shape[0], dtype=torch.bool, device=molecule["pos"].device)
+ molecule["fixed_node_type"] = torch.ones(molecule["node_type"].shape[0], dtype=torch.bool, device=molecule["node_type"].device)
+ molecule["fixed_halfedge_type"] = torch.ones(molecule["halfedge_type"].shape[0], dtype=torch.bool, device=molecule["halfedge_type"].device)
+ molecule["fixed_halfdist"] = torch.zeros(molecule["halfedge_type"].shape[0], device=molecule["halfedge_type"].device)
+ return self.sample(molecule, pocket)
+
+ def predict_structure_based_drug_design(self,
+ pocket: Featurized[Pocket]
+ ) -> List[Molecule]:
+ molecule = self.create_dummy_molecule(pocket)
+ molecule["fixed_pos"] = torch.zeros(molecule["pos"].shape[0], dtype=torch.bool, device=molecule["pos"].device)
+ molecule["fixed_node_type"] = torch.zeros(molecule["node_type"].shape[0], dtype=torch.bool, device=molecule["node_type"].device)
+ molecule["fixed_halfedge_type"] = torch.zeros(molecule["halfedge_type"].shape[0], dtype=torch.bool, device=molecule["halfedge_type"].device)
+ molecule["fixed_halfdist"] = torch.zeros(molecule["halfedge_type"].shape[0], device=molecule["halfedge_type"].device)
+ return self.sample(molecule, pocket)
\ No newline at end of file
diff --git a/open_biomed/models/functional_model.py b/open_biomed/models/functional_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a17ea24a43cb75a6e83d5e771a5b1ac634043d4
--- /dev/null
+++ b/open_biomed/models/functional_model.py
@@ -0,0 +1,116 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Tuple, Union
+from enum import Enum, auto
+
+import torch
+
+from open_biomed.data import Molecule, Protein, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import MoleculeFeaturizer, ProteinFeaturizer, TextFeaturizer, Featurized
+
+class MoleculeModel(BaseModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(MoleculeModel, self).__init__(model_cfg)
+
+ @abstractmethod
+ def get_molecule_processor(self) -> Tuple[MoleculeFeaturizer, Collator]:
+ raise NotImplementedError
+
+class MoleculeEncoder(MoleculeModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(MoleculeEncoder, self).__init__(model_cfg)
+
+ @abstractmethod
+ def encode_loss(self, label: Featurized[Molecule], **kwargs) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def encode_molecule(self, molecule: Union[List[Molecule], Any]) -> torch.Tensor:
+ raise NotImplementedError
+
+class MoleculeDecoder(MoleculeModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(MoleculeDecoder, self).__init__(model_cfg)
+
+ @abstractmethod
+ def generate_loss(self, label: Featurized[Molecule], **kwargs) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def generate_molecule(self, **kwargs) -> List[Molecule]:
+ raise NotImplementedError
+
+class ProteinModel(BaseModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ @abstractmethod
+ def get_protein_processor(self) -> Tuple[ProteinFeaturizer, Collator]:
+ raise NotImplementedError
+
+class ProteinEncoder(ProteinModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ @abstractmethod
+ def encode_protein(self, protein: Union[Featurized[Protein], List[Protein]], **kwargs) -> torch.Tensor:
+ raise NotImplementedError
+
+class ProteinDecoder(ProteinModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ @abstractmethod
+ def generate_loss(self, label: Featurized[Protein], **kwargs) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def generate_protein(self, **kwargs) -> List[Protein]:
+ raise NotImplementedError
+
+class TextEncoder(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super(TextEncoder, self).__init__(model_cfg)
+
+ @abstractmethod
+ def get_text_processor(self) -> Tuple[TextFeaturizer, Collator]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def encode_text(self, text: Union[List[Text], Any]) -> torch.Tensor:
+ raise NotImplementedError
+
+class ChatModel(BaseModel, ABC):
+ class Role(Enum):
+ USER = auto()
+ ASSISTANT = auto()
+
+ role_dict = {
+ Role.USER: "USER",
+ Role.ASSISTANT: "ASSISTANT",
+ }
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+ self.messages = []
+
+ def add_message(self, role: Role, message: Optional[str]) -> None:
+ self.messages.append([role, message])
+
+ def compose_context(self):
+ ret = self.config.system_prompt + self.config.sep_tokens + " "
+ for role, message in self.messages:
+ if message:
+ ret += self.role_dict[role] + ": " + message + " " + self.config.sep_tokens + " "
+ else:
+ ret += self.role_dict[role] + ": "
+ return ret
+
+ @abstractmethod
+ def chat(self, user_prompt: Text) -> Text:
+ raise NotImplementedError
+
+ @abstractmethod
+ def reset(self) -> None:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/functional_model_registry.py b/open_biomed/models/functional_model_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d32eb0b83b1e7dab5df833f67ccef5117814334
--- /dev/null
+++ b/open_biomed/models/functional_model_registry.py
@@ -0,0 +1,13 @@
+from open_biomed.models.protein.progen.progen import ProGen
+from open_biomed.models.text.pretrained_lm import PretrainedLMForTextEncoding
+
+TEXT_ENCODER_REGISTRY = {
+ "pretrained_lm": PretrainedLMForTextEncoding,
+}
+TEXT_DECODER_REGISTRY = {}
+MOLECULE_ENCODER_REGISTRY = {}
+MOLECULE_DECODER_REGISTRY = {}
+PROTEIN_ENCODER_REGISTRY = {}
+PROTEIN_DECODER_REGISTRY = {
+ "progen": ProGen,
+}
\ No newline at end of file
diff --git a/open_biomed/models/misc.py b/open_biomed/models/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7c884e2604cf4537870f9deb72be79c3b6c170e
--- /dev/null
+++ b/open_biomed/models/misc.py
@@ -0,0 +1,42 @@
+from typing import List, Optional, Union
+
+import logging
+
+import torch
+import torch.nn as nn
+
+ACT_FN = {
+ "softmax": nn.Softmax(),
+ "tanh": nn.Tanh(),
+ "relu": nn.ReLU(),
+}
+
+class MLP(nn.Module):
+ def __init__(self,
+ input_dim: int=256,
+ hidden_dim: Union[List[int], int]=[],
+ output_dim: int=2,
+ num_layers: Optional[int]=None,
+ act_fn: str="relu",
+ layer_norm: bool=False,
+ act_last: bool=False,
+ ) -> None:
+ super().__init__()
+ layers = []
+ if isinstance(hidden_dim, int):
+ hidden_dim = [hidden_dim] * num_layers
+ else:
+ if num_layers is not None:
+ logging.warn("Hidden dim is specified, num_layers is ignored")
+ hidden_dim = [input_dim] + hidden_dim + [output_dim]
+ num_layers = len(hidden_dim) - 1
+ for i in range(num_layers):
+ layers.append(nn.Linear(hidden_dim[i], hidden_dim[i + 1]))
+ if i < num_layers - 1 or act_last:
+ if layer_norm:
+ layers.append(nn.LayerNorm(hidden_dim[i + 1]))
+ layers.append(ACT_FN[act_fn])
+ self.network = nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.network(x)
\ No newline at end of file
diff --git a/open_biomed/models/molecule/graphmvp.py b/open_biomed/models/molecule/graphmvp.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cabd47f264d91a9ff65b6ef26eb21aa15ac769b
--- /dev/null
+++ b/open_biomed/models/molecule/graphmvp.py
@@ -0,0 +1,531 @@
+# from https://github.com/chao1224/GraphMVP/tree/main/src_classification/models/molecule_gnn_model.py
+import logging
+logger = logging.getLogger(__name__)
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch_geometric.nn import (MessagePassing, global_add_pool, global_max_pool, global_mean_pool)
+from torch_geometric.nn.inits import glorot, zeros
+from torch_geometric.utils import add_self_loops, softmax
+from torch_scatter import scatter_add
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.task_models import MoleculePropertyPredictionModel
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import ClassLabelFeaturizer, Featurized
+from open_biomed.utils.mol_featurizer import MolGraphFeaturizer
+from open_biomed.utils.collator import DPCollator, ClassLabelCollator
+from open_biomed.utils.misc import concatenate_tokens
+import selfies as sf
+
+from typing import Any, Dict, Generic, List, TypeVar
+
+num_atom_type = 120 # including the extra mask tokens
+num_chirality_tag = 3
+
+num_bond_type = 6 # including aromatic and self-loop edge, and extra masked tokens
+num_bond_direction = 3
+
+
+class GINConv(MessagePassing):
+ def __init__(self, emb_dim, aggr="add"):
+ super(GINConv, self).__init__()
+ self.aggr = aggr
+ self.mlp = nn.Sequential(nn.Linear(emb_dim, 2 * emb_dim),
+ nn.ReLU(),
+ nn.Linear(2 * emb_dim, emb_dim))
+ self.edge_embedding1 = nn.Embedding(num_bond_type, emb_dim)
+ self.edge_embedding2 = nn.Embedding(num_bond_direction, emb_dim)
+
+ nn.init.xavier_uniform_(self.edge_embedding1.weight.data)
+ nn.init.xavier_uniform_(self.edge_embedding2.weight.data)
+
+ def forward(self, x, edge_index, edge_attr):
+ edge_index = add_self_loops(edge_index, num_nodes=x.size(0))
+
+ self_loop_attr = torch.zeros(x.size(0), 2)
+ self_loop_attr[:, 0] = 4 # bond type for self-loop edge
+ self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype)
+ edge_attr = torch.cat((edge_attr, self_loop_attr), dim=0)
+
+ edge_embeddings = self.edge_embedding1(edge_attr[:, 0]) + \
+ self.edge_embedding2(edge_attr[:, 1])
+
+ return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings)
+
+ def message(self, x_j, edge_attr):
+ return x_j + edge_attr
+
+ def update(self, aggr_out):
+ return self.mlp(aggr_out)
+
+
+class GCNConv(MessagePassing):
+ def __init__(self, emb_dim, aggr="add"):
+ super(GCNConv, self).__init__()
+ self.aggr = aggr
+ self.emb_dim = emb_dim
+ self.linear = nn.Linear(emb_dim, emb_dim)
+ self.edge_embedding1 = nn.Embedding(num_bond_type, emb_dim)
+ self.edge_embedding2 = nn.Embedding(num_bond_direction, emb_dim)
+
+ nn.init.xavier_uniform_(self.edge_embedding1.weight.data)
+ nn.init.xavier_uniform_(self.edge_embedding2.weight.data)
+
+ def norm(self, edge_index, num_nodes, dtype):
+ ### assuming that self-loops have been already added in edge_index
+ edge_weight = torch.ones((edge_index.size(1),), dtype=dtype, device=edge_index.device)
+ row, col = edge_index
+ deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
+ deg_inv_sqrt = deg.pow(-0.5)
+ deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
+
+ return deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
+
+ def forward(self, x, edge_index, edge_attr):
+ # add self loops in the edge space
+ edge_index = add_self_loops(edge_index, num_nodes=x.size(0))
+
+ # add features corresponding to self-loop edges.
+ self_loop_attr = torch.zeros(x.size(0), 2)
+ self_loop_attr[:, 0] = 4 # bond type for self-loop edge
+ self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype)
+
+ edge_attr = torch.cat((edge_attr, self_loop_attr), dim=0)
+ edge_embeddings = self.edge_embedding1(edge_attr[:, 0]) + \
+ self.edge_embedding2(edge_attr[:, 1])
+
+ norm = self.norm(edge_index[0], x.size(0), x.dtype)
+
+ x = self.linear(x)
+
+ return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings, norm=norm)
+
+ def message(self, x_j, edge_attr, norm):
+ return norm.view(-1, 1) * (x_j + edge_attr)
+
+
+class GATConv(MessagePassing):
+ def __init__(self, emb_dim, heads=2, negative_slope=0.2, aggr="add"):
+ super(GATConv, self).__init__(node_dim=0)
+ self.aggr = aggr
+ self.heads = heads
+ self.emb_dim = emb_dim
+ self.negative_slope = negative_slope
+
+ self.weight_linear = nn.Linear(emb_dim, heads * emb_dim)
+ self.att = nn.Parameter(torch.Tensor(1, heads, 2 * emb_dim))
+
+ self.bias = nn.Parameter(torch.Tensor(emb_dim))
+
+ self.edge_embedding1 = nn.Embedding(num_bond_type, heads * emb_dim)
+ self.edge_embedding2 = nn.Embedding(num_bond_direction, heads * emb_dim)
+
+ nn.init.xavier_uniform_(self.edge_embedding1.weight.data)
+ nn.init.xavier_uniform_(self.edge_embedding2.weight.data)
+
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ glorot(self.att)
+ zeros(self.bias)
+
+ def forward(self, x, edge_index, edge_attr):
+ # add self loops in the edge space
+ edge_index = add_self_loops(edge_index, num_nodes=x.size(0))
+
+ # add features corresponding to self-loop edges.
+ self_loop_attr = torch.zeros(x.size(0), 2)
+ self_loop_attr[:, 0] = 4 # bond type for self-loop edge
+ self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype)
+
+ edge_attr = torch.cat((edge_attr, self_loop_attr), dim=0)
+ edge_embeddings = self.edge_embedding1(edge_attr[:, 0]) + \
+ self.edge_embedding2(edge_attr[:, 1])
+
+ x = self.weight_linear(x)
+ return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings)
+
+ def message(self, edge_index, x_i, x_j, edge_attr):
+ x_i = x_i.view(-1, self.heads, self.emb_dim)
+ x_j = x_j.view(-1, self.heads, self.emb_dim)
+ edge_attr = edge_attr.view(-1, self.heads, self.emb_dim)
+ x_j += edge_attr
+
+ alpha = (torch.cat([x_i, x_j], dim=-1) * self.att).sum(dim=-1)
+ alpha = F.leaky_relu(alpha, self.negative_slope)
+ alpha = softmax(alpha, edge_index[0])
+
+ return x_j * alpha.view(-1, self.heads, 1)
+
+ def update(self, aggr_out):
+ aggr_out = aggr_out.mean(dim=1)
+ aggr_out += self.bias
+ return aggr_out
+
+
+class GraphSAGEConv(MessagePassing):
+ def __init__(self, emb_dim, aggr="mean"):
+ super(GraphSAGEConv, self).__init__()
+ self.aggr = aggr
+
+ self.emb_dim = emb_dim
+ self.linear = nn.Linear(emb_dim, emb_dim)
+ self.edge_embedding1 = nn.Embedding(num_bond_type, emb_dim)
+ self.edge_embedding2 = nn.Embedding(num_bond_direction, emb_dim)
+
+ nn.init.xavier_uniform_(self.edge_embedding1.weight.data)
+ nn.init.xavier_uniform_(self.edge_embedding2.weight.data)
+
+
+ def forward(self, x, edge_index, edge_attr):
+ # add self loops in the edge space
+ edge_index = add_self_loops(edge_index, num_nodes=x.size(0))
+
+ # add features corresponding to self-loop edges.
+ self_loop_attr = torch.zeros(x.size(0), 2)
+ self_loop_attr[:, 0] = 4 # bond type for self-loop edge
+ self_loop_attr = self_loop_attr.to(edge_attr.device).to(edge_attr.dtype)
+ edge_attr = torch.cat((edge_attr, self_loop_attr), dim=0)
+
+ edge_embeddings = self.edge_embedding1(edge_attr[:, 0]) + \
+ self.edge_embedding2(edge_attr[:, 1])
+
+ x = self.linear(x)
+
+ return self.propagate(edge_index[0], x=x, edge_attr=edge_embeddings)
+
+ def message(self, x_j, edge_attr):
+ return x_j + edge_attr
+
+ def update(self, aggr_out):
+ return F.normalize(aggr_out, p=2, dim=-1)
+
+# same with class GNN of GraphMVP
+class GNNGraphMVP(nn.Module):
+ def __init__(self, num_layer, emb_dim, JK="last", drop_ratio=0., gnn_type="gin"):
+ if num_layer < 2:
+ raise ValueError("Number of GNN layers must be greater than 1.")
+
+ super(GNNGraphMVP, self).__init__()
+ self.drop_ratio = drop_ratio
+ self.num_layer = num_layer
+ self.JK = JK
+
+ self.x_embedding1 = nn.Embedding(num_atom_type, emb_dim)
+ self.x_embedding2 = nn.Embedding(num_chirality_tag, emb_dim)
+
+ nn.init.xavier_uniform_(self.x_embedding1.weight.data)
+ nn.init.xavier_uniform_(self.x_embedding2.weight.data)
+
+ ###List of MLPs
+ self.gnns = nn.ModuleList()
+ for layer in range(num_layer):
+ if gnn_type == "gin":
+ self.gnns.append(GINConv(emb_dim, aggr="add"))
+ elif gnn_type == "gcn":
+ self.gnns.append(GCNConv(emb_dim))
+ elif gnn_type == "gat":
+ self.gnns.append(GATConv(emb_dim))
+ elif gnn_type == "graphsage":
+ self.gnns.append(GraphSAGEConv(emb_dim))
+
+ ###List of batchnorms
+ self.batch_norms = nn.ModuleList()
+ for layer in range(num_layer):
+ self.batch_norms.append(nn.BatchNorm1d(emb_dim))
+
+ # def forward(self, x, edge_index, edge_attr):
+ def forward(self, *argv):
+ if len(argv) == 3:
+ x, edge_index, edge_attr = argv[0], argv[1], argv[2]
+ elif len(argv) == 1:
+ data = argv[0]
+ x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
+ else:
+ raise ValueError("unmatched number of arguments.")
+
+ x = self.x_embedding1(x[:, 0]) + self.x_embedding2(x[:, 1])
+
+ h_list = [x]
+ for layer in range(self.num_layer):
+ h = self.gnns[layer](h_list[layer], edge_index, edge_attr)
+ h = self.batch_norms[layer](h)
+ # h = F.dropout(F.relu(h), self.drop_ratio, training = self.training)
+ if layer == self.num_layer - 1:
+ # remove relu for the last layer
+ h = F.dropout(h, self.drop_ratio, training=self.training)
+ else:
+ h = F.dropout(F.relu(h), self.drop_ratio, training=self.training)
+ h_list.append(h)
+
+ ### Different implementations of Jk-concat
+ if self.JK == "concat":
+ node_representation = torch.cat(h_list, dim=1)
+ elif self.JK == "last":
+ node_representation = h_list[-1]
+ elif self.JK == "max":
+ h_list = [h.unsqueeze_(0) for h in h_list]
+ node_representation = torch.max(torch.cat(h_list, dim=0), dim=0)[0]
+ elif self.JK == "sum":
+ h_list = [h.unsqueeze_(0) for h in h_list]
+ node_representation = torch.sum(torch.cat(h_list, dim=0), dim=0)[0]
+ else:
+ raise ValueError("not implemented.")
+ return node_representation
+
+# same with class GNN_graphpred of GraphMVP
+class GraphMVP(MoleculePropertyPredictionModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(GraphMVP, self).__init__(model_cfg)
+ self.main_model = GNNGraphMVP(
+ num_layer=model_cfg["gin_num_layers"],
+ emb_dim=model_cfg["gin_hidden_dim"],
+ JK=model_cfg["JK"],
+ drop_ratio=model_cfg["drop_ratio"],
+ gnn_type=model_cfg["gnn_type"]
+ )
+
+ if model_cfg["gin_num_layers"] < 2:
+ raise ValueError("# layers must > 1.")
+
+ self.num_layer = model_cfg["gin_num_layers"]
+ self.emb_dim = model_cfg["gin_hidden_dim"]
+ self.JK = model_cfg["JK"]
+
+ # Different kind of graph pooling
+ if model_cfg["graph_pooling"] == "sum":
+ self.pool = global_add_pool
+ elif model_cfg["graph_pooling"] == "mean":
+ self.pool = global_mean_pool
+ elif model_cfg["graph_pooling"] == "max":
+ self.pool = global_max_pool
+ else:
+ raise ValueError("Invalid graph pooling type.")
+
+ # For graph-level binary classification
+ self.mult = 1
+
+ if self.JK == "concat":
+ self.graph_pred_linear = nn.Linear(self.mult * (self.num_layer + 1) * self.emb_dim,
+ self.num_tasks)
+ else:
+ self.graph_pred_linear = nn.Linear(self.mult * self.emb_dim, self.num_tasks)
+
+ self.criterion = nn.BCEWithLogitsLoss(reduction='none')
+
+ self.featurizers = {
+ "molecule": MolGraphFeaturizer(
+ {'name': 'BaseGNN'}
+ ),
+ "classlabel": ClassLabelFeaturizer()
+ }
+ self.collators = {
+ #"molecule": DPCollator({'modality': ['structure'], 'featurizer': {'structure': {'name': 'BaseGNN'}}}),
+ "molecule": DPCollator(),
+ "classlabel": ClassLabelCollator()
+ }
+
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+
+
+ def from_pretrained(self, model_file):
+ self.main_model.load_state_dict(torch.load(model_file))
+ return
+
+ def get_graph_representation(self, *argv):
+ if len(argv) == 4:
+ x, edge_index, edge_attr, batch = argv[0], argv[1], argv[2], argv[3]
+ elif len(argv) == 1:
+ data = argv[0]
+ x, edge_index, edge_attr, batch = data.x, data.edge_index, \
+ data.edge_attr, data.batch
+ else:
+ raise ValueError("unmatched number of arguments.")
+
+ node_representation = self.molecule_model(x, edge_index, edge_attr)
+ graph_representation = self.pool(node_representation, batch)
+ pred = self.graph_pred_linear(graph_representation)
+
+ return graph_representation, pred
+
+ def encode_mol(self, *argv):
+ if len(argv) == 4:
+ x, edge_index, edge_attr, batch = argv[0], argv[1], argv[2], argv[3]
+ elif len(argv) == 1:
+ data = argv[0]
+ x, edge_index, edge_attr, batch = data.x, data.edge_index, \
+ data.edge_attr, data.batch
+ else:
+ raise ValueError("unmatched number of arguments.")
+
+ node_representation = self.main_model(x, edge_index, edge_attr)
+ graph_representation = self.pool(node_representation, batch)
+ output = self.graph_pred_linear(graph_representation)
+
+ return output
+
+ def load_state_dict(self, state_dict, strict=True):
+ return self.main_model.load_state_dict(state_dict, strict)
+
+ def forward_molecule_property_prediction(self,
+ molecule,
+ label
+ ) -> Dict[str, torch.Tensor]:
+ pred = self.encode_mol(molecule)
+ y = label.view(pred.shape).to(torch.float64)
+ # Whether y is non-null or not.
+ is_valid = y ** 2 > 0
+ # Loss matrix
+ loss_mat = self.criterion(pred.double(), (y + 1) / 2) # -1变成0
+ # loss matrix after removing null target
+ loss_mat = torch.where(
+ is_valid, loss_mat,
+ torch.zeros(loss_mat.shape).to(loss_mat.device).to(loss_mat.dtype))
+ loss = torch.sum(loss_mat) / torch.sum(is_valid)
+ return {"loss":loss}
+
+ @torch.no_grad()
+ def predict_molecule_property_prediction(self,
+ molecule
+ ) -> Dict[str, torch.Tensor]:
+ pred = self.encode_mol(molecule)
+ pred = torch.sigmoid(pred)
+ result_list = [[round(x.item(), 4) for x in row] for row in pred.cpu()]
+ return result_list
+
+
+# just regression version of GraphMVP
+class GraphMVPRegression(MoleculePropertyPredictionModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(GraphMVPRegression, self).__init__(model_cfg)
+ self.main_model = GNNGraphMVP(
+ num_layer=model_cfg["gin_num_layers"],
+ emb_dim=model_cfg["gin_hidden_dim"],
+ JK=model_cfg["JK"],
+ drop_ratio=model_cfg["drop_ratio"],
+ gnn_type=model_cfg["gnn_type"]
+ )
+
+ if model_cfg["gin_num_layers"] < 2:
+ raise ValueError("# layers must > 1.")
+
+ self.num_layer = model_cfg["gin_num_layers"]
+ self.emb_dim = model_cfg["gin_hidden_dim"]
+ self.JK = model_cfg["JK"]
+
+ # Different kind of graph pooling
+ if model_cfg["graph_pooling"] == "sum":
+ self.pool = global_add_pool
+ elif model_cfg["graph_pooling"] == "mean":
+ self.pool = global_mean_pool
+ elif model_cfg["graph_pooling"] == "max":
+ self.pool = global_max_pool
+ else:
+ raise ValueError("Invalid graph pooling type.")
+
+ # For graph-level binary classification
+ self.mult = 1
+
+ if self.JK == "concat":
+ self.graph_pred_linear = nn.Linear(self.mult * (self.num_layer + 1) * self.emb_dim,
+ self.num_tasks)
+ else:
+ self.graph_pred_linear = nn.Linear(self.mult * self.emb_dim, self.num_tasks)
+
+ self.criterion = nn.MSELoss(reduction='none')
+ # self.criterion = nn.L1Loss(reduction='none')
+ # self.criterion = nn.HuberLoss(reduction='none', delta=10)
+
+ self.featurizers = {
+ "molecule": MolGraphFeaturizer(
+ {'name': 'BaseGNN'}
+ ),
+ "classlabel": ClassLabelFeaturizer()
+ }
+ self.collators = {
+ #"molecule": DPCollator({'modality': ['structure'], 'featurizer': {'structure': {'name': 'BaseGNN'}}}),
+ "molecule": DPCollator(),
+ "classlabel": ClassLabelCollator()
+ }
+
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+
+
+ def from_pretrained(self, model_file):
+ self.main_model.load_state_dict(torch.load(model_file))
+ return
+
+ def get_graph_representation(self, *argv):
+ if len(argv) == 4:
+ x, edge_index, edge_attr, batch = argv[0], argv[1], argv[2], argv[3]
+ elif len(argv) == 1:
+ data = argv[0]
+ x, edge_index, edge_attr, batch = data.x, data.edge_index, \
+ data.edge_attr, data.batch
+ else:
+ raise ValueError("unmatched number of arguments.")
+
+ node_representation = self.molecule_model(x, edge_index, edge_attr)
+ graph_representation = self.pool(node_representation, batch)
+ pred = self.graph_pred_linear(graph_representation)
+
+ return graph_representation, pred
+
+ def encode_mol(self, *argv):
+ if len(argv) == 4:
+ x, edge_index, edge_attr, batch = argv[0], argv[1], argv[2], argv[3]
+ elif len(argv) == 1:
+ data = argv[0]
+ x, edge_index, edge_attr, batch = data.x, data.edge_index, \
+ data.edge_attr, data.batch
+ else:
+ raise ValueError("unmatched number of arguments.")
+
+ node_representation = self.main_model(x, edge_index, edge_attr)
+ graph_representation = self.pool(node_representation, batch)
+ output = self.graph_pred_linear(graph_representation)
+
+ return output
+
+ def load_state_dict(self, state_dict, strict=True):
+ return self.main_model.load_state_dict(state_dict, strict)
+
+ def forward_molecule_property_prediction(self,
+ molecule,
+ label
+ ) -> Dict[str, torch.Tensor]:
+ # Whether y is non-null or not.
+ # Regression version
+ pred = self.encode_mol(molecule) # Get predictions from model
+ y = label.view(pred.shape).to(torch.double) # Ensure label matches prediction shape
+
+ # Whether y is non-null or not (if you still need to handle missing values)
+ is_valid = ~torch.isnan(y)
+
+ # Use MSE loss for regression
+ loss_mat = self.criterion(pred.double(), y)
+
+ # Handle invalid/missing targets if needed
+ loss_mat = torch.where(
+ is_valid, loss_mat,
+ torch.zeros(loss_mat.shape).to(loss_mat.device).to(loss_mat.dtype))
+ loss = torch.sum(loss_mat) / torch.sum(is_valid)
+
+ return {"loss": loss}
+
+
+ @torch.no_grad()
+ def predict_molecule_property_prediction(self,
+ molecule
+ ) -> Dict[str, torch.Tensor]:
+ pred = self.encode_mol(molecule)
+ result_list = [[round(x.item(), 4) for x in row] for row in pred.cpu()]
+ return result_list
\ No newline at end of file
diff --git a/open_biomed/models/molecule/molcraft.py b/open_biomed/models/molecule/molcraft.py
new file mode 100644
index 0000000000000000000000000000000000000000..16c2da1c748544f8b55acff17fd6eabed076efc0
--- /dev/null
+++ b/open_biomed/models/molecule/molcraft.py
@@ -0,0 +1,1187 @@
+from typing import Any, Dict, List, Optional
+
+import itertools
+import logging
+import numpy as np
+try:
+ from openbabel import openbabel as ob
+ ob.obErrorLog.SetOutputLevel(0)
+except ImportError:
+ logging.warning("OpenBabel is not installed. The MolCRAFT model will not be able to decode generated molecules.")
+from rdkit import Chem, Geometry
+from rdkit.Chem import AllChem
+from scipy.spatial.distance import pdist, squareform
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch_geometric.data import Data
+from torch_geometric.nn import radius_graph, knn_graph
+from torch_scatter import scatter_softmax, scatter_sum
+from tqdm import tqdm
+
+from open_biomed.data import Molecule, MoleculeConstructError, Pocket, estimate_ligand_atom_num
+from open_biomed.models.task_models.structure_based_drug_design import StructureBasedDrugDesignModel
+from open_biomed.utils.collator import PygCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurized, MoleculeFeaturizer, PocketFeaturizer
+from open_biomed.utils.misc import safe_index
+
+### Borrowed from https://github.com/AlgoMole/MolCRAFT
+
+MAP_ATOM_TYPE_AROMATIC_TO_INDEX = {
+ (1, False): 0,
+ (6, False): 1,
+ (6, True): 2,
+ (7, False): 3,
+ (7, True): 4,
+ (8, False): 5,
+ (8, True): 6,
+ (9, False): 7,
+ (15, False): 8,
+ (15, True): 9,
+ (16, False): 10,
+ (16, True): 11,
+ (17, False): 12
+}
+MAP_INDEX_TO_ATOM_TYPE_AROMATIC = {v: k for k, v in MAP_ATOM_TYPE_AROMATIC_TO_INDEX.items()}
+
+# Featurizers
+class MolCRAFTMoleculeFeaturizer(MoleculeFeaturizer):
+ def __init__(self, pos_norm=1.0) -> None:
+ super().__init__()
+ self.pos_norm = pos_norm
+
+ def __call__(self, molecule: Molecule) -> Dict[str, Any]:
+ molecule._add_rdmol()
+ rdmol = molecule.rdmol
+ node_feat_list = []
+ for atom in rdmol.GetAtoms():
+ node_feat_list.append(safe_index(MAP_ATOM_TYPE_AROMATIC_TO_INDEX, (atom.GetAtomicNum(), atom.GetIsAromatic())))
+ node_feat = F.one_hot(torch.LongTensor(node_feat_list), num_classes=len(MAP_ATOM_TYPE_AROMATIC_TO_INDEX)).float()
+ pos = torch.tensor(molecule.conformer, dtype=torch.float32)
+ pocket_center = pos.mean(dim=0)
+ pos -= pocket_center
+ pos /= self.pos_norm
+
+ return Data(**{
+ "atom_feature": node_feat,
+ "pos": pos,
+ })
+
+ def reconstruct_from_generated(self, xyz: List[List[float]], atomic_nums: List[int], aromatic: List[bool], basic_mode: bool=True) -> Chem.RWMol:
+ def fixup(atoms: List[Any], mol: ob.OBMol, indicators: List[bool]) -> List[Chem.Atom]:
+ '''Set atom properties to match channel. Keep doing this
+ to beat openbabel over the head with what we want to happen.'''
+
+ """
+ for now, indicators only include 'is_aromatic'
+ """
+ mol.SetAromaticPerceived(True) # avoid perception
+ for i, atom in enumerate(atoms):
+ if indicators is not None:
+ if indicators[i]:
+ atom.SetAromatic(True)
+ atom.SetHyb(2)
+ else:
+ atom.SetAromatic(False)
+
+ if (atom.GetAtomicNum() in (7, 8)) and atom.IsInRing(): # Nitrogen, Oxygen
+ # this is a little iffy, ommitting until there is more evidence it is a net positive
+ # we don't have aromatic types for nitrogen, but if it
+ # is in a ring with aromatic carbon mark it aromatic as well
+ acnt = 0
+ for nbr in ob.OBAtomAtomIter(atom):
+ if nbr.IsAromatic():
+ acnt += 1
+ if acnt > 1:
+ atom.SetAromatic(True)
+
+ mol = ob.OBMol()
+ mol.BeginModify()
+ atoms = []
+ for xyz, t in zip(xyz, atomic_nums):
+ x, y, z = xyz
+ # ch = struct.channels[t]
+ atom = mol.NewAtom()
+ atom.SetAtomicNum(t)
+ atom.SetVector(x, y, z)
+ atoms.append(atom)
+ fixup(atoms, mol, aromatic)
+
+ # Connect the dots
+ '''Custom implementation of ConnectTheDots. This is similar to
+ OpenBabel's version, but is more willing to make long bonds
+ (up to maxbond long) to keep the molecule connected. It also
+ attempts to respect atom type information from struct.
+ atoms and struct need to correspond in their order
+ Assumes no hydrogens or existing bonds.
+ '''
+ pt = AllChem.GetPeriodicTable()
+ covalent_factor = 1.3
+
+ mol.BeginModify()
+
+ # just going to to do n^2 comparisons, can worry about efficiency later
+ coords = np.array([(a.GetX(), a.GetY(), a.GetZ()) for a in atoms])
+ dists = squareform(pdist(coords))
+ # types = [struct.channels[t].name for t in struct.c]
+
+ for i, j in itertools.combinations(range(len(atoms)), 2):
+ a = atoms[i]
+ b = atoms[j]
+ a_r = ob.GetCovalentRad(a.GetAtomicNum()) * covalent_factor
+ b_r = ob.GetCovalentRad(b.GetAtomicNum()) * covalent_factor
+ if dists[i, j] < a_r + b_r:
+ flag = 0
+ if aromatic[i] and aromatic[j]:
+ flag = ob.OB_AROMATIC_BOND
+ mol.AddBond(a.GetIdx(), b.GetIdx(), 1, flag)
+
+ atom_maxb = {}
+ for (i, a) in enumerate(atoms):
+ # set max valance to the smallest max allowed by openbabel or rdkit
+ # since we want the molecule to be valid for both (rdkit is usually lower)
+ maxb = min(ob.GetMaxBonds(a.GetAtomicNum()), pt.GetDefaultValence(a.GetAtomicNum()))
+
+ nbrs_of_elem = 0
+ for nbr in ob.OBAtomAtomIter(a):
+ if nbr.GetAtomicNum() == 8:
+ nbrs_of_elem += 1
+ if a.GetAtomicNum() == 16: # sulfone check
+ if nbrs_of_elem >= 2:
+ maxb = 6
+
+ atom_maxb[a.GetIdx()] = maxb
+
+ # remove any impossible bonds between halogens
+ for bond in ob.OBMolBondIter(mol):
+ a1 = bond.GetBeginAtom()
+ a2 = bond.GetEndAtom()
+ if atom_maxb[a1.GetIdx()] == 1 and atom_maxb[a2.GetIdx()] == 1:
+ mol.DeleteBond(bond)
+
+ def get_bond_info(biter):
+ '''Return bonds sorted by their distortion'''
+ bonds = [b for b in biter]
+ binfo = []
+ for bond in bonds:
+ bdist = bond.GetLength()
+ # compute how far away from optimal we are
+ a1 = bond.GetBeginAtom()
+ a2 = bond.GetEndAtom()
+ ideal = ob.GetCovalentRad(a1.GetAtomicNum()) + ob.GetCovalentRad(a2.GetAtomicNum())
+ stretch = bdist / ideal
+ binfo.append((stretch, bond))
+ binfo.sort(reverse=True, key=lambda t: t[0]) # most stretched bonds first
+ return binfo
+
+ def reachable_r(a, b, seenbonds):
+ '''Recursive helper.'''
+
+ for nbr in ob.OBAtomAtomIter(a):
+ bond = a.GetBond(nbr).GetIdx()
+ if bond not in seenbonds:
+ seenbonds.add(bond)
+ if nbr == b:
+ return True
+ elif reachable_r(nbr, b, seenbonds):
+ return True
+ return False
+
+ def reachable(a, b):
+ '''Return true if atom b is reachable from a without using the bond between them.'''
+ if a.GetExplicitDegree() == 1 or b.GetExplicitDegree() == 1:
+ return False # this is the _only_ bond for one atom
+ # otherwise do recursive traversal
+ seenbonds = set([a.GetBond(b).GetIdx()])
+ return reachable_r(a, b, seenbonds)
+
+
+ def forms_small_angle(a, b, cutoff=60):
+ '''Return true if bond between a and b is part of a small angle
+ with a neighbor of a only.'''
+
+ for nbr in ob.OBAtomAtomIter(a):
+ if nbr != b:
+ degrees = b.GetAngle(a, nbr)
+ if degrees < cutoff:
+ return True
+ return False
+
+ binfo = get_bond_info(ob.OBMolBondIter(mol))
+ # now eliminate geometrically poor bonds
+ for stretch, bond in binfo:
+
+ # can we remove this bond without disconnecting the molecule?
+ a1 = bond.GetBeginAtom()
+ a2 = bond.GetEndAtom()
+
+ # as long as we aren't disconnecting, let's remove things
+ # that are excessively far away (0.45 from ConnectTheDots)
+ # get bonds to be less than max allowed
+ # also remove tight angles, because that is what ConnectTheDots does
+ if stretch > 1.2 or forms_small_angle(a1, a2) or forms_small_angle(a2, a1):
+ # don't fragment the molecule
+ if not reachable(a1, a2):
+ continue
+ mol.DeleteBond(bond)
+
+ # prioritize removing hypervalency causing bonds, do more valent
+ # constrained atoms first since their bonds introduce the most problems
+ # with reachability (e.g. oxygen)
+ hypers = [(atom_maxb[a.GetIdx()], a.GetExplicitValence() - atom_maxb[a.GetIdx()], a) for a in atoms]
+ hypers = sorted(hypers, key=lambda aa: (aa[0], -aa[1]))
+ for mb, diff, a in hypers:
+ if a.GetExplicitValence() <= atom_maxb[a.GetIdx()]:
+ continue
+ binfo = get_bond_info(ob.OBAtomBondIter(a))
+ for stretch, bond in binfo:
+
+ if stretch < 0.9: # the two atoms are too closed to remove the bond
+ continue
+ # can we remove this bond without disconnecting the molecule?
+ a1 = bond.GetBeginAtom()
+ a2 = bond.GetEndAtom()
+
+ # get right valence
+ if a1.GetExplicitValence() > atom_maxb[a1.GetIdx()] or a2.GetExplicitValence() > atom_maxb[a2.GetIdx()]:
+ # don't fragment the molecule
+ if not reachable(a1, a2):
+ continue
+ mol.DeleteBond(bond)
+ if a.GetExplicitValence() <= atom_maxb[a.GetIdx()]:
+ break # let nbr atoms choose what bonds to throw out
+
+ mol.EndModify()
+ fixup(atoms, mol, aromatic)
+
+ mol.AddPolarHydrogens()
+ mol.PerceiveBondOrders()
+ fixup(atoms, mol, aromatic)
+
+ for (i, a) in enumerate(atoms):
+ ob.OBAtomAssignTypicalImplicitHydrogens(a)
+ fixup(atoms, mol, aromatic)
+
+ mol.AddHydrogens()
+ fixup(atoms, mol, aromatic)
+
+ # make rings all aromatic if majority of carbons are aromatic
+ for ring in ob.OBMolRingIter(mol):
+ if 5 <= ring.Size() <= 6:
+ carbon_cnt = 0
+ aromatic_ccnt = 0
+ for ai in ring._path:
+ a = mol.GetAtom(ai)
+ if a.GetAtomicNum() == 6:
+ carbon_cnt += 1
+ if a.IsAromatic():
+ aromatic_ccnt += 1
+ if aromatic_ccnt >= carbon_cnt / 2 and aromatic_ccnt != ring.Size():
+ # set all ring atoms to be aromatic
+ for ai in ring._path:
+ a = mol.GetAtom(ai)
+ a.SetAromatic(True)
+
+ # bonds must be marked aromatic for smiles to match
+ for bond in ob.OBMolBondIter(mol):
+ a1 = bond.GetBeginAtom()
+ a2 = bond.GetEndAtom()
+ if a1.IsAromatic() and a2.IsAromatic():
+ bond.SetAromatic(True)
+
+ mol.PerceiveBondOrders()
+
+ def calc_valence(rdatom):
+ '''Can call GetExplicitValence before sanitize, but need to
+ know this to fix up the molecule to prevent sanitization failures'''
+ cnt = 0.0
+ for bond in rdatom.GetBonds():
+ cnt += bond.GetBondTypeAsDouble()
+ return cnt
+
+ def convert_ob_mol_to_rd_mol(ob_mol, struct=None):
+ '''Convert OBMol to RDKit mol, fixing up issues'''
+ ob_mol.DeleteHydrogens()
+ n_atoms = ob_mol.NumAtoms()
+ rd_mol = AllChem.RWMol()
+ rd_conf = AllChem.Conformer(n_atoms)
+
+ for ob_atom in ob.OBMolAtomIter(ob_mol):
+ rd_atom = AllChem.Atom(ob_atom.GetAtomicNum())
+ # TODO copy format charge
+ if ob_atom.IsAromatic() and ob_atom.IsInRing() and ob_atom.MemberOfRingSize() <= 6:
+ # don't commit to being aromatic unless rdkit will be okay with the ring status
+ # (this can happen if the atoms aren't fit well enough)
+ rd_atom.SetIsAromatic(True)
+ i = rd_mol.AddAtom(rd_atom)
+ ob_coords = ob_atom.GetVector()
+ x = ob_coords.GetX()
+ y = ob_coords.GetY()
+ z = ob_coords.GetZ()
+ rd_coords = Geometry.Point3D(x, y, z)
+ rd_conf.SetAtomPosition(i, rd_coords)
+
+ rd_mol.AddConformer(rd_conf)
+
+ for ob_bond in ob.OBMolBondIter(ob_mol):
+ i = ob_bond.GetBeginAtomIdx() - 1
+ j = ob_bond.GetEndAtomIdx() - 1
+ bond_order = ob_bond.GetBondOrder()
+ if bond_order == 1:
+ rd_mol.AddBond(i, j, AllChem.BondType.SINGLE)
+ elif bond_order == 2:
+ rd_mol.AddBond(i, j, AllChem.BondType.DOUBLE)
+ elif bond_order == 3:
+ rd_mol.AddBond(i, j, AllChem.BondType.TRIPLE)
+ else:
+ raise Exception('unknown bond order {}'.format(bond_order))
+
+ if ob_bond.IsAromatic():
+ bond = rd_mol.GetBondBetweenAtoms(i, j)
+ bond.SetIsAromatic(True)
+
+ rd_mol = AllChem.RemoveHs(rd_mol, sanitize=False)
+
+ pt = AllChem.GetPeriodicTable()
+ # if double/triple bonds are connected to hypervalent atoms, decrement the order
+
+ # TODO: fix seg fault
+ # if struct is not None:
+ # positions = struct
+ positions = rd_mol.GetConformer().GetPositions()
+ nonsingles = []
+ for bond in rd_mol.GetBonds():
+ if bond.GetBondType() == AllChem.BondType.DOUBLE or bond.GetBondType() == AllChem.BondType.TRIPLE:
+ i = bond.GetBeginAtomIdx()
+ j = bond.GetEndAtomIdx()
+ # TODO: ugly fix
+ dist = np.linalg.norm(positions[i] - positions[j])
+ nonsingles.append((dist, bond))
+ nonsingles.sort(reverse=True, key=lambda t: t[0])
+
+ for (d, bond) in nonsingles:
+ a1 = bond.GetBeginAtom()
+ a2 = bond.GetEndAtom()
+
+ if calc_valence(a1) > pt.GetDefaultValence(a1.GetAtomicNum()) or \
+ calc_valence(a2) > pt.GetDefaultValence(a2.GetAtomicNum()):
+ btype = AllChem.BondType.SINGLE
+ if bond.GetBondType() == AllChem.BondType.TRIPLE:
+ btype = AllChem.BondType.DOUBLE
+ bond.SetBondType(btype)
+
+ # fix up special cases
+ for atom in rd_mol.GetAtoms():
+ # set nitrogens with 4 neighbors to have a charge
+ if atom.GetAtomicNum() == 7 and atom.GetDegree() == 4:
+ atom.SetFormalCharge(1)
+
+ # check if there are any carbon atoms with 2 double C-C bonds
+ # if so, convert one to a single bond
+ if atom.GetAtomicNum() == 6 and atom.GetDegree() == 4:
+ cnt = 0
+ i = atom.GetIdx()
+ for nbr in atom.GetNeighbors():
+ if nbr.GetAtomicNum() == 6:
+ j = nbr.GetIdx()
+ bond = rd_mol.GetBondBetweenAtoms(i, j)
+ if bond.GetBondType() == AllChem.BondType.DOUBLE:
+ cnt += 1
+ if cnt == 2:
+ for nbr in atom.GetNeighbors():
+ if nbr.GetAtomicNum() == 6:
+ j = nbr.GetIdx()
+ bond = rd_mol.GetBondBetweenAtoms(i, j)
+ if bond.GetBondType() == AllChem.BondType.DOUBLE:
+ bond.SetBondType(AllChem.BondType.SINGLE)
+ break
+
+ rd_mol = AllChem.AddHs(rd_mol, addCoords=True)
+ # TODO: fix seg fault
+ positions = rd_mol.GetConformer().GetPositions()
+ center = np.mean(positions[np.all(np.isfinite(positions), axis=1)], axis=0)
+ for atom in rd_mol.GetAtoms():
+ i = atom.GetIdx()
+ pos = positions[i]
+ if not np.all(np.isfinite(pos)):
+ # hydrogens on C fragment get set to nan (shouldn't, but they do)
+ rd_mol.GetConformer().SetAtomPosition(i, center)
+
+ try:
+ AllChem.SanitizeMol(rd_mol, AllChem.SANITIZE_ALL ^ AllChem.SANITIZE_KEKULIZE)
+ except:
+ raise MoleculeConstructError("Failed to construct molecule from generated coordinates and atoms.")
+
+ # but at some point stop trying to enforce our aromaticity -
+ # openbabel and rdkit have different aromaticity models so they
+ # won't always agree. Remove any aromatic bonds to non-aromatic atoms
+ for bond in rd_mol.GetBonds():
+ a1 = bond.GetBeginAtom()
+ a2 = bond.GetEndAtom()
+ if bond.GetIsAromatic():
+ if not a1.GetIsAromatic() or not a2.GetIsAromatic():
+ bond.SetIsAromatic(False)
+ elif a1.GetIsAromatic() and a2.GetIsAromatic():
+ bond.SetIsAromatic(True)
+
+ return rd_mol
+
+ def postprocess_rd_mol_1(rdmol):
+ UPGRADE_BOND_ORDER = {AllChem.BondType.SINGLE: AllChem.BondType.DOUBLE, AllChem.BondType.DOUBLE: AllChem.BondType.TRIPLE}
+ rdmol = AllChem.RemoveHs(rdmol)
+
+ # Construct bond nbh list
+ nbh_list = {}
+ for bond in rdmol.GetBonds():
+ begin, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
+ if begin not in nbh_list:
+ nbh_list[begin] = [end]
+ else:
+ nbh_list[begin].append(end)
+
+ if end not in nbh_list:
+ nbh_list[end] = [begin]
+ else:
+ nbh_list[end].append(begin)
+
+ # Fix missing bond-order
+ for atom in rdmol.GetAtoms():
+ idx = atom.GetIdx()
+ num_radical = atom.GetNumRadicalElectrons()
+ if num_radical > 0:
+ for j in nbh_list[idx]:
+ if j <= idx: continue
+ nb_atom = rdmol.GetAtomWithIdx(j)
+ nb_radical = nb_atom.GetNumRadicalElectrons()
+ if nb_radical > 0:
+ bond = rdmol.GetBondBetweenAtoms(idx, j)
+ bond.SetBondType(UPGRADE_BOND_ORDER[bond.GetBondType()])
+ nb_atom.SetNumRadicalElectrons(nb_radical - 1)
+ num_radical -= 1
+ atom.SetNumRadicalElectrons(num_radical)
+
+ num_radical = atom.GetNumRadicalElectrons()
+ if num_radical > 0:
+ atom.SetNumRadicalElectrons(0)
+ num_hs = atom.GetNumExplicitHs()
+ atom.SetNumExplicitHs(num_hs + num_radical)
+
+ return rdmol
+
+
+ def postprocess_rd_mol_2(rdmol):
+ rdmol_edit = AllChem.RWMol(rdmol)
+
+ ring_info = rdmol.GetRingInfo()
+ ring_info.AtomRings()
+ rings = [set(r) for r in ring_info.AtomRings()]
+ for i, ring_a in enumerate(rings):
+ if len(ring_a) == 3:
+ non_carbon = []
+ atom_by_symb = {}
+ for atom_idx in ring_a:
+ symb = rdmol.GetAtomWithIdx(atom_idx).GetSymbol()
+ if symb != 'C':
+ non_carbon.append(atom_idx)
+ if symb not in atom_by_symb:
+ atom_by_symb[symb] = [atom_idx]
+ else:
+ atom_by_symb[symb].append(atom_idx)
+ if len(non_carbon) == 2:
+ rdmol_edit.RemoveBond(*non_carbon)
+ if 'O' in atom_by_symb and len(atom_by_symb['O']) == 2:
+ rdmol_edit.RemoveBond(*atom_by_symb['O'])
+ rdmol_edit.GetAtomWithIdx(atom_by_symb['O'][0]).SetNumExplicitHs(
+ rdmol_edit.GetAtomWithIdx(atom_by_symb['O'][0]).GetNumExplicitHs() + 1
+ )
+ rdmol_edit.GetAtomWithIdx(atom_by_symb['O'][1]).SetNumExplicitHs(
+ rdmol_edit.GetAtomWithIdx(atom_by_symb['O'][1]).GetNumExplicitHs() + 1
+ )
+ rdmol = rdmol_edit.GetMol()
+
+ for atom in rdmol.GetAtoms():
+ if atom.GetFormalCharge() > 0:
+ atom.SetFormalCharge(0)
+
+ return rdmol
+
+ rd_mol = convert_ob_mol_to_rd_mol(mol, struct=xyz)
+ try:
+ # Post-processing
+ rd_mol = postprocess_rd_mol_1(rd_mol)
+ rd_mol = postprocess_rd_mol_2(rd_mol)
+ except:
+ raise MoleculeConstructError("Failed to construct molecule from generated coordinates and atoms.")
+
+ return rd_mol
+
+ def decode(self, preds: Dict[str, torch.Tensor], pocket_center: Optional[List[float]]) -> Optional[Molecule]:
+ pos = preds["pos"] * self.pos_norm
+ if pocket_center is not None:
+ pos += pocket_center
+
+ preds["pos"] = pos.cpu().numpy().tolist()
+ preds["is_aromatic"] = [MAP_INDEX_TO_ATOM_TYPE_AROMATIC[x][1] for x in preds["atom_type"].cpu().numpy()]
+ preds["atom_type"] = [MAP_INDEX_TO_ATOM_TYPE_AROMATIC[x][0] for x in preds["atom_type"].cpu().numpy()]
+
+ try:
+ rdmol = self.reconstruct_from_generated(preds["pos"], preds["atom_type"], preds["is_aromatic"])
+ molecule = Molecule.from_rdmol(rdmol)
+ except Exception as e:
+ logging.warn(e)
+ molecule = None
+
+ return molecule
+
+class MolCRAFTPocketFeaturizer(PocketFeaturizer):
+ def __init__(self, pos_norm: float=1.0) -> None:
+ super().__init__()
+ self.atomic_numbers = torch.LongTensor([1, 6, 7, 8, 16, 34]) # H, C, N, O, S, Se
+ self.max_num_aa = 20
+ self.pos_norm = pos_norm
+
+ def __call__(self, pocket: Pocket) -> Dict[str, Any]:
+ elements = torch.LongTensor([atom["atomic_number"] for atom in pocket.atoms])
+ elements_one_hot = (elements.view(-1, 1) == self.atomic_numbers.view(1, -1)).long()
+ aa_type = torch.LongTensor([atom["aa_type"] for atom in pocket.atoms])
+ aa_one_hot = F.one_hot(aa_type, num_classes=self.max_num_aa)
+ is_backbone = torch.LongTensor([atom["is_backbone"] for atom in pocket.atoms]).unsqueeze(-1)
+ x = torch.cat([elements_one_hot, aa_one_hot, is_backbone], dim=-1).float()
+ pos = torch.tensor(pocket.conformer, dtype=torch.float32)
+ pocket_center = pos.mean(dim=0)
+ pos -= pocket_center
+ pos /= self.pos_norm
+
+ return Data(**{
+ "atom_feature": x,
+ "pos": pos,
+ "pocket_center": pocket_center.unsqueeze(0),
+ "estimated_ligand_num_atoms": torch.tensor(estimate_ligand_atom_num(pocket)).unsqueeze(0),
+ })
+
+NONLINEARITIES = {
+ "tanh": nn.Tanh(),
+ "relu": nn.ReLU(),
+ "softplus": nn.Softplus(),
+ "elu": nn.ELU(),
+ 'silu': nn.SiLU()
+}
+
+class GaussianSmearing(nn.Module):
+ def __init__(self, start=0.0, stop=5.0, num_gaussians=50, fixed_offset=True):
+ super(GaussianSmearing, self).__init__()
+ self.start = start
+ self.stop = stop
+ self.num_gaussians = num_gaussians
+ if fixed_offset:
+ # customized offset
+ offset = torch.tensor([0, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.5, 4, 4.5, 5, 5.5, 6, 7, 8, 9, 10])
+ else:
+ offset = torch.linspace(start, stop, num_gaussians)
+ self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2
+ self.register_buffer('offset', offset)
+
+ def __repr__(self):
+ return f'GaussianSmearing(start={self.start}, stop={self.stop}, num_gaussians={self.num_gaussians})'
+
+ def forward(self, dist):
+ dist = dist.view(-1, 1) - self.offset.view(1, -1)
+ return torch.exp(self.coeff * torch.pow(dist, 2))
+
+class MLP(nn.Module):
+ """MLP with the same hidden dim across all layers."""
+
+ def __init__(self, in_dim, out_dim, hidden_dim, num_layer=2, norm=True, act_fn='relu', act_last=False):
+ super().__init__()
+ layers = []
+ for layer_idx in range(num_layer):
+ if layer_idx == 0:
+ layers.append(nn.Linear(in_dim, hidden_dim))
+ elif layer_idx == num_layer - 1:
+ layers.append(nn.Linear(hidden_dim, out_dim))
+ else:
+ layers.append(nn.Linear(hidden_dim, hidden_dim))
+ if layer_idx < num_layer - 1 or act_last:
+ if norm:
+ layers.append(nn.LayerNorm(hidden_dim))
+ layers.append(NONLINEARITIES[act_fn])
+ self.net = nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.net(x)
+
+class BaseX2HAttLayer(nn.Module):
+ def __init__(self, input_dim, hidden_dim, output_dim, n_heads, edge_feat_dim, r_feat_dim,
+ act_fn='relu', norm=True, ew_net_type='r', out_fc=True):
+ super().__init__()
+ self.input_dim = input_dim
+ self.hidden_dim = hidden_dim
+ self.output_dim = output_dim
+ self.n_heads = n_heads
+ self.act_fn = act_fn
+ self.edge_feat_dim = edge_feat_dim
+ self.r_feat_dim = r_feat_dim
+ self.ew_net_type = ew_net_type
+ self.out_fc = out_fc
+
+ # attention key func
+ kv_input_dim = input_dim * 2 + edge_feat_dim + r_feat_dim
+ self.hk_func = MLP(kv_input_dim, output_dim, hidden_dim, norm=norm, act_fn=act_fn)
+
+ # attention value func
+ self.hv_func = MLP(kv_input_dim, output_dim, hidden_dim, norm=norm, act_fn=act_fn)
+
+ # attention query func
+ self.hq_func = MLP(input_dim, output_dim, hidden_dim, norm=norm, act_fn=act_fn)
+ if ew_net_type == 'r':
+ self.ew_net = nn.Sequential(nn.Linear(r_feat_dim, 1), nn.Sigmoid())
+ elif ew_net_type == 'm':
+ self.ew_net = nn.Sequential(nn.Linear(output_dim, 1), nn.Sigmoid())
+
+ if self.out_fc:
+ self.node_output = MLP(2 * hidden_dim, hidden_dim, hidden_dim, norm=norm, act_fn=act_fn)
+
+ def forward(self, h, r_feat, edge_feat, edge_index, e_w=None):
+ N = h.size(0)
+ src, dst = edge_index
+ hi, hj = h[dst], h[src]
+
+ # multi-head attention
+ # decide inputs of k_func and v_func
+ kv_input = torch.cat([r_feat, hi, hj], -1)
+ if edge_feat is not None:
+ kv_input = torch.cat([edge_feat, kv_input], -1)
+
+ # compute k
+ k = self.hk_func(kv_input).view(-1, self.n_heads, self.output_dim // self.n_heads)
+ # compute v
+ v = self.hv_func(kv_input)
+
+ if self.ew_net_type == 'r':
+ e_w = self.ew_net(r_feat)
+ elif self.ew_net_type == 'm':
+ e_w = self.ew_net(v[..., :self.hidden_dim])
+ elif e_w is not None:
+ e_w = e_w.view(-1, 1)
+ else:
+ e_w = 1.
+ v = v * e_w
+ v = v.view(-1, self.n_heads, self.output_dim // self.n_heads)
+
+ # compute q
+ q = self.hq_func(h).view(-1, self.n_heads, self.output_dim // self.n_heads)
+
+ # compute attention weights
+ alpha = scatter_softmax((q[dst] * k / np.sqrt(k.shape[-1])).sum(-1), dst, dim=0,
+ dim_size=N) # [num_edges, n_heads]
+
+ # perform attention-weighted message-passing
+ m = alpha.unsqueeze(-1) * v # (E, heads, H_per_head)
+ output = scatter_sum(m, dst, dim=0, dim_size=N) # (N, heads, H_per_head)
+ output = output.view(-1, self.output_dim)
+ if self.out_fc:
+ output = self.node_output(torch.cat([output, h], -1))
+
+ output = output + h
+ return output
+
+
+class BaseH2XAttLayer(nn.Module):
+ def __init__(self, input_dim, hidden_dim, output_dim, n_heads, edge_feat_dim, r_feat_dim,
+ act_fn='relu', norm=True, ew_net_type='r'):
+ super().__init__()
+ self.input_dim = input_dim
+ self.hidden_dim = hidden_dim
+ self.output_dim = output_dim
+ self.n_heads = n_heads
+ self.edge_feat_dim = edge_feat_dim
+ self.r_feat_dim = r_feat_dim
+ self.act_fn = act_fn
+ self.ew_net_type = ew_net_type
+
+ kv_input_dim = input_dim * 2 + edge_feat_dim + r_feat_dim
+
+ self.xk_func = MLP(kv_input_dim, output_dim, hidden_dim, norm=norm, act_fn=act_fn)
+ self.xv_func = MLP(kv_input_dim, self.n_heads, hidden_dim, norm=norm, act_fn=act_fn)
+ self.xq_func = MLP(input_dim, output_dim, hidden_dim, norm=norm, act_fn=act_fn)
+ if ew_net_type == 'r':
+ self.ew_net = nn.Sequential(nn.Linear(r_feat_dim, 1), nn.Sigmoid())
+
+ def forward(self, h, rel_x, r_feat, edge_feat, edge_index, e_w=None):
+ N = h.size(0)
+ src, dst = edge_index
+ hi, hj = h[dst], h[src]
+
+ # multi-head attention
+ # decide inputs of k_func and v_func
+ kv_input = torch.cat([r_feat, hi, hj], -1)
+ if edge_feat is not None:
+ kv_input = torch.cat([edge_feat, kv_input], -1)
+
+ k = self.xk_func(kv_input).view(-1, self.n_heads, self.output_dim // self.n_heads)
+ v = self.xv_func(kv_input)
+ if self.ew_net_type == 'r':
+ e_w = self.ew_net(r_feat)
+ elif self.ew_net_type == 'm':
+ e_w = 1.
+ elif e_w is not None:
+ e_w = e_w.view(-1, 1)
+ else:
+ e_w = 1.
+ v = v * e_w
+
+ v = v.unsqueeze(-1) * rel_x.unsqueeze(1) # (xi - xj) [n_edges, n_heads, 3]
+ q = self.xq_func(h).view(-1, self.n_heads, self.output_dim // self.n_heads)
+
+ # Compute attention weights
+ alpha = scatter_softmax((q[dst] * k / np.sqrt(k.shape[-1])).sum(-1), dst, dim=0, dim_size=N) # (E, heads)
+
+ # Perform attention-weighted message-passing
+ m = alpha.unsqueeze(-1) * v # (E, heads, 3)
+ output = scatter_sum(m, dst, dim=0, dim_size=N) # (N, heads, 3)
+ return output.mean(1) # [num_nodes, 3]
+
+def outer_product(*vectors):
+ for index, vector in enumerate(vectors):
+ if index == 0:
+ out = vector.unsqueeze(-1)
+ else:
+ out = out * vector.unsqueeze(1)
+ out = out.view(out.shape[0], -1).unsqueeze(-1)
+ return out.squeeze()
+
+
+class AttentionLayerO2TwoUpdateNodeGeneral(nn.Module):
+ def __init__(self, hidden_dim, n_heads, num_r_gaussian, edge_feat_dim, act_fn='relu', norm=True,
+ num_x2h=1, num_h2x=1, r_min=0., r_max=10., num_node_types=8,
+ ew_net_type='r', x2h_out_fc=True, sync_twoup=False):
+ super().__init__()
+ self.hidden_dim = hidden_dim
+ self.n_heads = n_heads
+ self.edge_feat_dim = edge_feat_dim
+ self.num_r_gaussian = num_r_gaussian
+ self.norm = norm
+ self.act_fn = act_fn
+ self.num_x2h = num_x2h
+ self.num_h2x = num_h2x
+ self.r_min, self.r_max = r_min, r_max
+ self.num_node_types = num_node_types
+ self.ew_net_type = ew_net_type
+ self.x2h_out_fc = x2h_out_fc
+ self.sync_twoup = sync_twoup
+
+ self.distance_expansion = GaussianSmearing(self.r_min, self.r_max, num_gaussians=num_r_gaussian)
+
+ self.x2h_layers = nn.ModuleList()
+ for i in range(self.num_x2h):
+ self.x2h_layers.append(
+ BaseX2HAttLayer(hidden_dim, hidden_dim, hidden_dim, n_heads, edge_feat_dim,
+ r_feat_dim=num_r_gaussian * 4,
+ act_fn=act_fn, norm=norm,
+ ew_net_type=self.ew_net_type, out_fc=self.x2h_out_fc)
+ )
+ self.h2x_layers = nn.ModuleList()
+ for i in range(self.num_h2x):
+ self.h2x_layers.append(
+ BaseH2XAttLayer(hidden_dim, hidden_dim, hidden_dim, n_heads, edge_feat_dim,
+ r_feat_dim=num_r_gaussian * 4,
+ act_fn=act_fn, norm=norm,
+ ew_net_type=self.ew_net_type)
+ )
+
+ def forward(self, h, x, edge_attr, edge_index, mask_ligand, e_w=None, fix_x=False):
+ src, dst = edge_index
+ if self.edge_feat_dim > 0:
+ edge_feat = edge_attr # shape: [#edges_in_batch, #bond_types]
+ else:
+ edge_feat = None
+
+ rel_x = x[dst] - x[src]
+ dist = torch.norm(rel_x, p=2, dim=-1, keepdim=True)
+
+ h_in = h
+ # 4 separate distance embedding for p-p, p-l, l-p, l-l
+ for i in range(self.num_x2h):
+ dist_feat = self.distance_expansion(dist)
+ dist_feat = outer_product(edge_attr, dist_feat)
+ h_out = self.x2h_layers[i](h_in, dist_feat, edge_feat, edge_index, e_w=e_w)
+ h_in = h_out
+ x2h_out = h_in
+
+ new_h = h if self.sync_twoup else x2h_out
+ for i in range(self.num_h2x):
+ dist_feat = self.distance_expansion(dist)
+ dist_feat = outer_product(edge_attr, dist_feat)
+ delta_x = self.h2x_layers[i](new_h, rel_x, dist_feat, edge_feat, edge_index, e_w=e_w)
+ if not fix_x:
+ x = x + delta_x * mask_ligand[:, None] # only ligand positions will be updated
+ rel_x = x[dst] - x[src]
+ dist = torch.norm(rel_x, p=2, dim=-1, keepdim=True)
+
+ return x2h_out, x
+
+
+class UniTransformerO2TwoUpdateGeneral(nn.Module):
+ def __init__(self, num_blocks, num_layers, hidden_dim, n_heads=1, knn=32,
+ num_r_gaussian=50, edge_feat_dim=0, num_node_types=8, act_fn='relu', norm=True,
+ cutoff_mode='radius', ew_net_type='r',
+ num_init_x2h=1, num_init_h2x=0, num_x2h=1, num_h2x=1, r_max=10., x2h_out_fc=True, sync_twoup=False, name='unio2net'):
+ super().__init__()
+ self.name = name
+ # Build the network
+ self.num_blocks = num_blocks
+ self.num_layers = num_layers
+ self.hidden_dim = hidden_dim
+ self.n_heads = n_heads
+ self.num_r_gaussian = num_r_gaussian
+ self.edge_feat_dim = edge_feat_dim
+ self.act_fn = act_fn
+ self.norm = norm
+ self.num_node_types = num_node_types
+ # radius graph / knn graph
+ self.cutoff_mode = cutoff_mode # [radius, none]
+ self.knn = knn
+ self.ew_net_type = ew_net_type # [r, m, none]
+
+ self.num_x2h = num_x2h
+ self.num_h2x = num_h2x
+ self.num_init_x2h = num_init_x2h
+ self.num_init_h2x = num_init_h2x
+ self.r_max = r_max
+ self.x2h_out_fc = x2h_out_fc
+ self.sync_twoup = sync_twoup
+ self.distance_expansion = GaussianSmearing(0., r_max, num_gaussians=num_r_gaussian)
+ if self.ew_net_type == 'global':
+ self.edge_pred_layer = MLP(num_r_gaussian, 1, hidden_dim)
+
+ self.init_h_emb_layer = self._build_init_h_layer()
+ self.base_block = self._build_share_blocks()
+
+ def __repr__(self):
+ return f'UniTransformerO2(num_blocks={self.num_blocks}, num_layers={self.num_layers}, n_heads={self.n_heads}, ' \
+ f'act_fn={self.act_fn}, norm={self.norm}, cutoff_mode={self.cutoff_mode}, ew_net_type={self.ew_net_type}, ' \
+ f'init h emb: {self.init_h_emb_layer.__repr__()} \n' \
+ f'base block: {self.base_block.__repr__()} \n' \
+ f'edge pred layer: {self.edge_pred_layer.__repr__() if hasattr(self, "edge_pred_layer") else "None"}) '
+
+ def _build_init_h_layer(self):
+ layer = AttentionLayerO2TwoUpdateNodeGeneral(
+ self.hidden_dim, self.n_heads, self.num_r_gaussian, self.edge_feat_dim, act_fn=self.act_fn, norm=self.norm,
+ num_x2h=self.num_init_x2h, num_h2x=self.num_init_h2x, r_max=self.r_max, num_node_types=self.num_node_types,
+ ew_net_type=self.ew_net_type, x2h_out_fc=self.x2h_out_fc, sync_twoup=self.sync_twoup,
+ )
+ return layer
+
+ def _build_share_blocks(self):
+ # Equivariant layers
+ base_block = []
+ for l_idx in range(self.num_layers):
+ layer = AttentionLayerO2TwoUpdateNodeGeneral(
+ self.hidden_dim, self.n_heads, self.num_r_gaussian, self.edge_feat_dim, act_fn=self.act_fn,
+ norm=self.norm,
+ num_x2h=self.num_x2h, num_h2x=self.num_h2x, r_max=self.r_max, num_node_types=self.num_node_types,
+ ew_net_type=self.ew_net_type, x2h_out_fc=self.x2h_out_fc, sync_twoup=self.sync_twoup,
+ )
+ base_block.append(layer)
+ return nn.ModuleList(base_block)
+
+ def _connect_edge(self, x, mask_ligand, batch):
+ if self.cutoff_mode == 'radius':
+ edge_index = radius_graph(x, r=self.r, batch=batch, flow='source_to_target')
+ elif self.cutoff_mode == 'knn':
+ edge_index = knn_graph(x, k=self.knn, batch=batch, flow='source_to_target')
+ else:
+ raise ValueError(f'Not supported cutoff mode: {self.cutoff_mode}')
+ return edge_index
+
+ @staticmethod
+ def _build_edge_type(edge_index, mask_ligand):
+ src, dst = edge_index
+ edge_type = torch.zeros(len(src)).to(edge_index)
+ n_src = mask_ligand[src] == 1
+ n_dst = mask_ligand[dst] == 1
+ edge_type[n_src & n_dst] = 0
+ edge_type[n_src & ~n_dst] = 1
+ edge_type[~n_src & n_dst] = 2
+ edge_type[~n_src & ~n_dst] = 3
+ edge_type = F.one_hot(edge_type, num_classes=4)
+ return edge_type
+
+ def forward(self, h, x, mask_ligand, batch, return_all=False, fix_x=False):
+ all_x = [x]
+ all_h = [h]
+
+ for b_idx in range(self.num_blocks):
+ edge_index = self._connect_edge(x, mask_ligand, batch)
+ src, dst = edge_index
+
+ # edge type (dim: 4)
+ edge_type = self._build_edge_type(edge_index, mask_ligand)
+ if self.ew_net_type == 'global':
+ dist = torch.norm(x[dst] - x[src], p=2, dim=-1, keepdim=True)
+ dist_feat = self.distance_expansion(dist)
+ logits = self.edge_pred_layer(dist_feat)
+ e_w = torch.sigmoid(logits)
+ else:
+ e_w = None
+
+ for l_idx, layer in enumerate(self.base_block):
+ h, x = layer(h, x, edge_type, edge_index, mask_ligand, e_w=e_w, fix_x=fix_x)
+ all_x.append(x)
+ all_h.append(h)
+
+ outputs = {'x': x, 'h': h}
+ if return_all:
+ outputs.update({'all_x': all_x, 'all_h': all_h})
+ return outputs
+
+class TimeEmbedLayer(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.time_emb = lambda x: x
+
+ def forward(self, t):
+ return self.time_emb(t)
+
+class ShiftedSoftplus(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.shift = torch.log(torch.tensor(2.0)).item()
+
+ def forward(self, x):
+ return F.softplus(x) - self.shift
+
+class MolCRAFT(StructureBasedDrugDesignModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(MolCRAFT, self).__init__(model_cfg)
+ self.config = model_cfg
+ self.node_indicator = getattr(model_cfg, 'node_indicator', False)
+ emb_dim = model_cfg.hidden_dim if not self.node_indicator else model_cfg.hidden_dim - 1
+
+ self.time_emb_layer = TimeEmbedLayer()
+ self.protein_atom_emb = nn.Linear(model_cfg.protein_atom_feature_dim, emb_dim)
+ self.ligand_atom_emb = nn.Linear(model_cfg.ligand_atom_feature_dim + 1, emb_dim)
+ self.unio2net = UniTransformerO2TwoUpdateGeneral(**model_cfg.unio2net.todict())
+ self.v_inference = nn.Sequential(
+ nn.Linear(model_cfg.hidden_dim, model_cfg.hidden_dim),
+ ShiftedSoftplus(),
+ nn.Linear(model_cfg.hidden_dim, model_cfg.ligand_atom_feature_dim),
+ ) # [hidden to 13]
+ self.sigma1_coord = torch.tensor(model_cfg.sigma1_coord, dtype=torch.float32)
+ self.beta1 = torch.tensor(model_cfg.beta1, dtype=torch.float32)
+
+ self.featurizers = {
+ "molecule": MolCRAFTMoleculeFeaturizer(pos_norm=model_cfg.pos_norm),
+ "pocket": MolCRAFTPocketFeaturizer(pos_norm=model_cfg.pos_norm),
+ }
+ self.collators = {
+ "molecule": PygCollator(follow_batch=["mu_pos"]),
+ "pocket": PygCollator(follow_batch=["pos"])
+ }
+
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+ def continuous_var_bayesian_update(self, t: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+ # Eq.(77): p_F(θ|x;t) ~ N (μ | γ(t)x, γ(t)(1 − γ(t))I)
+ gamma = (1 - torch.pow(self.sigma1_coord, 2 * t)) # [B]
+ mu = gamma * x + torch.sqrt(gamma* (1 - gamma)) * torch.randn_like(x)
+ return mu, gamma
+
+ def discrete_var_bayesian_update(self, t: torch.Tensor, x: torch.Tensor, K: int) -> torch.Tensor:
+ # Eq.(182): β(t) = t**2 β(1)
+ beta = (self.config.beta1 * (t**2)) # (B,)
+
+ # Eq.(185): p_F(θ|x;t) = E_{N(y | β(t)(Ke_x−1), β(t)KI)} δ (θ − softmax(y))
+ # can be sampled by first drawing y ~ N(y | β(t)(Ke_x−1), β(t)KI)
+ # then setting θ = softmax(y)
+ one_hot_x = x # (N, K)
+ mean = beta * (K * one_hot_x - 1)
+ std = (beta * K).sqrt()
+ eps = torch.randn_like(mean)
+ y = mean + std * eps
+ theta = F.softmax(y, dim=-1)
+ return theta
+
+ def compose_context(self, h_protein, h_ligand, pos_protein, pos_ligand, batch_protein, batch_ligand):
+ # previous version has problems when ligand atom types are fixed
+ # (due to sorting randomly in case of same element)
+
+ batch_ctx = torch.cat([batch_protein, batch_ligand], dim=0)
+ sort_idx = torch.sort(batch_ctx, stable=True).indices
+
+ mask_ligand = torch.cat([
+ torch.zeros([batch_protein.size(0)], device=batch_protein.device).bool(),
+ torch.ones([batch_ligand.size(0)], device=batch_ligand.device).bool(),
+ ], dim=0)[sort_idx]
+
+ batch_ctx = batch_ctx[sort_idx]
+ h_ctx = torch.cat([h_protein, h_ligand], dim=0)[sort_idx] # (N_protein+N_ligand, H)
+ pos_ctx = torch.cat([pos_protein, pos_ligand], dim=0)[sort_idx] # (N_protein+N_ligand, 3)
+
+ return h_ctx, pos_ctx, batch_ctx, mask_ligand
+
+ def interdependency_modeling(
+ self,
+ time,
+ protein_pos, # transform from the orginal BFN codebase
+ protein_v, # transform from
+ batch_protein, # index for protein
+ theta_h_t,
+ mu_pos_t,
+ batch_ligand, # index for ligand
+ gamma_coord,
+ return_all=False, # legacy from targetdiff
+ fix_x=False,
+ ):
+ """
+ Compute output distribution parameters for p_O (x' | θ; t) (x_hat or k^(d) logits).
+ Draw output_sample = x' ~ p_O (x' | θ; t).
+ continuous x ~ δ(x - x_hat(θ, t))
+ discrete k^(d) ~ softmax(Ψ^(d)(θ, t))_k
+ Args:
+ time: [node_num x batch_size, 1] := [N_ligand, 1]
+ protein_pos: [node_num x batch_size, 3] := [N_protein, 3]
+ protein_v: [node_num x batch_size, protein_atom_feature_dim] := [N_protein, 27]
+ batch_protein: [node_num x batch_size] := [N_protein]
+ theta_h_t: [node_num x batch_size, atom_type] := [N_ligand, 13]
+ mu_pos_t: [node_num x batch_size, 3] := [N_ligand, 3]
+ batch_ligand: [node_num x batch_size] := [N_ligand]
+ gamma_coord: [node_num x batch_size, 1] := [N_ligand, 1]
+ """
+ theta_h_t = 2 * theta_h_t - 1 # from 1/K \in [0,1] to 2/K-1 \in [-1,1]
+
+ # ---------for targetdiff-----------
+ init_ligand_v = theta_h_t
+ # time embedding
+ time_emb = self.time_emb_layer(time)
+ input_ligand_feat = torch.cat([init_ligand_v, time_emb], -1)
+
+ h_protein = self.protein_atom_emb(protein_v) # [N_protein, self.hidden_dim - 1]
+ init_ligand_h = self.ligand_atom_emb(input_ligand_feat) # [N_ligand, self.hidden_dim - 1]
+
+ if self.node_indicator:
+ h_protein = torch.cat(
+ [h_protein, torch.zeros(len(h_protein), 1).to(h_protein)], -1
+ ) # [N_ligand, self.hidden_dim]
+ init_ligand_h = torch.cat(
+ [init_ligand_h, torch.ones(len(init_ligand_h), 1).to(h_protein)], -1
+ ) # [N_ligand, self.hidden_dim]
+
+ h_all, pos_all, batch_all, mask_ligand = self.compose_context(
+ h_protein=h_protein,
+ h_ligand=init_ligand_h,
+ pos_protein=protein_pos,
+ pos_ligand=mu_pos_t,
+ batch_protein=batch_protein,
+ batch_ligand=batch_ligand,
+ )
+ # get the context for the protein and ligand, while the ligand is h is noisy (h_t)/ pos is also the noise version. (pos_t)
+
+ # time = 2 * time - 1
+ outputs = self.unio2net(
+ h_all, pos_all, mask_ligand, batch_all, return_all=return_all, fix_x=fix_x
+ )
+ final_pos, final_h = outputs["x"], outputs["h"]
+ final_ligand_pos, final_ligand_h = final_pos[mask_ligand], final_h[mask_ligand]
+ final_ligand_v = self.v_inference(final_ligand_h) # [N_ligand, 13]
+
+ # 1. for continuous, network outputs eps_hat(θ, t)
+ # Eq.(84): x_hat(θ, t) = μ / γ(t) − \sqrt{(1 − γ(t)) / γ(t)} * eps_hat(θ, t)
+ # 2. for discrete, network outputs Ψ(θ, t)
+ # take softmax will do
+ return final_ligand_pos, F.softmax(final_ligand_v, dim=-1), torch.zeros_like(mu_pos_t)
+
+ def create_dummy_molecule(self, pocket: Featurized[Pocket]) -> Featurized[Molecule]:
+ num_atoms = pocket["estimated_ligand_num_atoms"].cpu()
+ return Data(**{
+ "mu_pos": torch.zeros(num_atoms.sum().item(), 3),
+ "theta_h": torch.ones(num_atoms.sum().item(), self.config.ligand_atom_feature_dim) / self.config.ligand_atom_feature_dim,
+ "mu_pos_batch": torch.repeat_interleave(torch.arange(len(num_atoms)), num_atoms),
+ }).to(pocket["atom_feature"].device)
+
+ @torch.no_grad()
+ def sample(self, molecule: Featurized[Molecule], pocket: Featurized[Pocket]) -> List[Molecule]:
+ in_traj, out_traj = [], []
+ device = molecule['mu_pos'].device
+ num_atoms = molecule['mu_pos_batch'].shape[0]
+
+ for step in tqdm(range(1, self.config.num_sample_steps + 1), desc="Sampling"):
+ t = torch.ones((num_atoms, 1), dtype=torch.float, device=device) * (step - 1) / self.config.num_sample_steps
+ gamma_coord = 1 - torch.pow(self.sigma1_coord, 2 * t)
+ in_traj.append((molecule["mu_pos"].clone(), molecule["theta_h"].clone()))
+ coord_pred, p0_h, _ = self.interdependency_modeling(
+ time=t,
+ protein_pos=pocket['pos'],
+ protein_v=pocket['atom_feature'],
+ batch_protein=pocket['pos_batch'],
+ batch_ligand=molecule['mu_pos_batch'],
+ theta_h_t=molecule['theta_h'],
+ mu_pos_t=molecule['mu_pos'],
+ gamma_coord=gamma_coord,
+ )
+ out_traj.append((coord_pred.detach().clone(), p0_h.detach().clone()))
+
+ t = torch.ones((num_atoms, 1), dtype=torch.float, device=device) * step / self.config.num_sample_steps
+ molecule['theta_h'] = self.discrete_var_bayesian_update(t, p0_h, self.config.ligand_atom_feature_dim)
+ molecule['mu_pos'], _ = self.continuous_var_bayesian_update(t, coord_pred)
+
+ """
+ if (step - 1) % 10 == 0:
+ print(molecule['mu_pos'][:10])
+ print(molecule['theta_h'][:10])
+ """
+
+ # Compute final output distribution parameters for p_O (x' | θ; t)
+ in_traj.append((molecule["mu_pos"].detach().clone(), molecule["theta_h"].detach().clone()))
+ mu_pos_final, p0_h_final, _ = self.interdependency_modeling(
+ time=torch.ones((num_atoms, 1)).to(device),
+ protein_pos=pocket['pos'],
+ protein_v=pocket['atom_feature'],
+ batch_protein=pocket['pos_batch'],
+ batch_ligand=molecule['mu_pos_batch'],
+ theta_h_t=molecule['theta_h'],
+ mu_pos_t=molecule['mu_pos'],
+ gamma_coord=1 - self.sigma1_coord ** 2, # γ(t) = 1 − (σ1**2) ** t
+ )
+ p0_h_final = torch.clamp(p0_h_final, min=1e-6)
+ out_traj.append((mu_pos_final.detach().clone(), p0_h_final.detach().clone()))
+
+ num_mols = molecule['mu_pos_batch'].max() + 1
+ in_traj_split, out_traj_split = [], []
+ out_molecules = []
+ for i in range(num_mols):
+ cur_molecule = {}
+ idx = torch.where(molecule['mu_pos_batch'] == i)[0]
+ in_traj_split.append({
+ "pos": torch.stack([in_traj[j][0][idx] for j in range(len(in_traj))], dim=0),
+ "atom_type": torch.stack([in_traj[j][1][idx] for j in range(len(in_traj))], dim=0),
+ })
+ out_traj_split.append({
+ "pos": torch.stack([out_traj[j][0][idx] for j in range(len(out_traj))], dim=0),
+ "atom_type": torch.stack([out_traj[j][1][idx] for j in range(len(out_traj))], dim=0),
+ })
+ cur_molecule = {
+ "pos": out_traj_split[i]["pos"][-1],
+ "atom_type": torch.argmax(out_traj_split[i]["atom_type"][-1], dim=-1),
+ }
+ out_molecules.append(self.featurizers["molecule"].decode(cur_molecule, pocket["pocket_center"][i]))
+ """
+ import pickle
+ from datetime import datetime
+ pickle.dump({
+ "in_traj": in_traj_split[0],
+ "out_traj": out_traj_split[0],
+ }, open(f"./tmp/debug_traj_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pkl", "wb"))
+ """
+ return out_molecules
+
+ def forward_structure_based_drug_design(self, pocket: Featurized[Pocket], label: Featurized[Molecule]) -> Dict[str, torch.Tensor]:
+ pass
+
+ @torch.no_grad()
+ def predict_structure_based_drug_design(self, pocket: Featurized[Pocket]) -> F.List[Molecule]:
+ molecule = self.create_dummy_molecule(pocket)
+ return self.sample(molecule, pocket)
\ No newline at end of file
diff --git a/open_biomed/models/protein/esmfold/esmfold.py b/open_biomed/models/protein/esmfold/esmfold.py
new file mode 100644
index 0000000000000000000000000000000000000000..1db00d16bf4745a33e60d23cc69dd05a809847e2
--- /dev/null
+++ b/open_biomed/models/protein/esmfold/esmfold.py
@@ -0,0 +1,107 @@
+import contextlib
+from typing import Any, Dict, List, Optional, Tuple
+
+from huggingface_hub import snapshot_download
+import logging
+import os
+import torch
+import torch.nn as nn
+from transformers import EsmTokenizer, DataCollatorWithPadding
+
+from open_biomed.models.protein.esmfold.modeling_esmfold import EsmForProteinFolding
+from open_biomed.data import Protein
+from open_biomed.models.task_models.protein_folding import ProteinFoldingModel
+from open_biomed.utils.collator import Collator, EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurizer, Featurized
+
+class ProteinEsmFeaturizer(Featurizer):
+ def __init__(self,
+ protein_tokenizer: EsmTokenizer,
+ max_length_protein: int=1024,
+ add_special_tokens: bool=False,
+ ) -> None:
+ super().__init__()
+ self.protein_tokenizer = protein_tokenizer
+ self.max_length_protein = max_length_protein
+ self.add_special_tokens = add_special_tokens
+
+ def __call__(self, protein: Protein) -> Dict[str, Any]:
+ sequence = protein.sequence
+
+ featurized = {}
+ featurized["protein"] = self.protein_tokenizer(
+ sequence,
+ max_length=self.max_length_protein,
+ truncation=True,
+ add_special_tokens=self.add_special_tokens,
+ )
+
+ return featurized
+
+ def get_attrs(self) -> List[str]:
+ return ["protein"]
+
+
+class EsmFold(ProteinFoldingModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(EsmFold, self).__init__(model_cfg)
+ if not os.path.exists(model_cfg.hf_model_name_or_path):
+ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+ logging.info("Repo not found. Try downloading from snapshot")
+ snapshot_download(repo_id="facebook/esmfold_v1", local_dir=model_cfg.hf_model_name_or_path, force_download=True)
+ # load tokenizer
+ logging.info("*** loading protein esm tokenizer...")
+ self.protein_tokenizer = EsmTokenizer.from_pretrained(model_cfg.hf_model_name_or_path)
+ # load esmfold model
+ logging.info("*** loading protein folding model...")
+ self.protein_model = EsmForProteinFolding.from_pretrained(model_cfg.hf_model_name_or_path, low_cpu_mem_usage=True)
+
+ if model_cfg.chunk_size:
+ self.protein_model.trunk.set_chunk_size(model_cfg.chunk_size)
+
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+ def maybe_autocast(self, device="cuda:0", dtype=torch.bfloat16):
+ # if on cpu, don't use autocast
+ # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
+ enable_autocast = device != torch.device("cpu")
+
+ if enable_autocast:
+ return torch.cuda.amp.autocast(dtype=dtype)
+ else:
+ return contextlib.nullcontext()
+
+ def featurizer_protein_folding(self) -> Tuple[Featurizer, Collator]:
+ return ProteinEsmFeaturizer(
+ protein_tokenizer=self.protein_tokenizer,
+ max_length_protein=self.config.max_length,
+ add_special_tokens=False,
+ ), EnsembleCollator({
+ "protein": DataCollatorWithPadding(
+ self.protein_tokenizer,
+ padding=True
+ )
+ })
+
+ def forward_protein_folding(self, protein: Featurized[Protein], **kwargs) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError("Training EsmFold is currently unavailable! Please see https://github.com/facebookresearch/esm/blob/main/esm/esmfold/v1/esmfold.py for more details.")
+
+ @torch.no_grad()
+ def predict_protein_folding(self,
+ protein: Featurized[Protein],
+ **kwargs
+ ) -> List[str]:
+ device = protein.input_ids.device
+
+ with self.maybe_autocast(device, dtype=torch.float16 if device != torch.device('cpu') and self.config.use_fp16_for_esm else torch.float32):
+ output = self.protein_model(protein.input_ids)
+
+ # TODO: convert output to open_biomed.data.Protein
+ output = self.protein_model.output_to_pdb(output)
+ output = [Protein.from_pdb(item.split("\n")) for item in output]
+
+ return output
+
\ No newline at end of file
diff --git a/open_biomed/models/protein/esmfold/modeling_esmfold.py b/open_biomed/models/protein/esmfold/modeling_esmfold.py
new file mode 100644
index 0000000000000000000000000000000000000000..029c56835fe72939f087c466659d146b76755eaf
--- /dev/null
+++ b/open_biomed/models/protein/esmfold/modeling_esmfold.py
@@ -0,0 +1,2319 @@
+# coding=utf-8
+# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+import sys
+from dataclasses import dataclass
+from functools import partial
+from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.nn import LayerNorm
+from transformers.deepspeed import is_deepspeed_available
+from transformers.modeling_outputs import ModelOutput
+from transformers.utils import (
+ ContextManagers,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_scipy_available,
+ logging,
+ replace_return_docstrings,
+)
+
+from transformers.models.esm.configuration_esm import EsmConfig
+from transformers.models.esm.modeling_esm import ESM_START_DOCSTRING, EsmModel, EsmPreTrainedModel
+from transformers.models.esm.openfold_utils import (
+ OFProtein,
+ Rigid,
+ Rotation,
+ atom14_to_atom37,
+ chunk_layer,
+ compute_predicted_aligned_error,
+ compute_tm,
+ frames_and_literature_positions_to_atom14_pos,
+ make_atom14_masks,
+ residue_constants,
+ to_pdb,
+ torsion_angles_to_frames,
+)
+
+logger = logging.get_logger(__name__)
+_CHECKPOINT_FOR_DOC = "facebook/esmfold_v1"
+_CONFIG_FOR_DOC = "EsmConfig"
+
+
+@dataclass
+class EsmForProteinFoldingOutput(ModelOutput):
+ """
+ Output type of [`EsmForProteinFoldingOutput`].
+
+ Args:
+ frames (`torch.FloatTensor`):
+ Output frames.
+ sidechain_frames (`torch.FloatTensor`):
+ Output sidechain frames.
+ unnormalized_angles (`torch.FloatTensor`):
+ Predicted unnormalized backbone and side chain torsion angles.
+ angles (`torch.FloatTensor`):
+ Predicted backbone and side chain torsion angles.
+ positions (`torch.FloatTensor`):
+ Predicted positions of the backbone and side chain atoms.
+ states (`torch.FloatTensor`):
+ Hidden states from the protein folding trunk.
+ s_s (`torch.FloatTensor`):
+ Per-residue embeddings derived by concatenating the hidden states of each layer of the ESM-2 LM stem.
+ s_z (`torch.FloatTensor`):
+ Pairwise residue embeddings.
+ distogram_logits (`torch.FloatTensor`):
+ Input logits to the distogram used to compute residue distances.
+ lm_logits (`torch.FloatTensor`):
+ Logits output by the ESM-2 protein language model stem.
+ aatype (`torch.FloatTensor`):
+ Input amino acids (AlphaFold2 indices).
+ atom14_atom_exists (`torch.FloatTensor`):
+ Whether each atom exists in the atom14 representation.
+ residx_atom14_to_atom37 (`torch.FloatTensor`):
+ Mapping between atoms in the atom14 and atom37 representations.
+ residx_atom37_to_atom14 (`torch.FloatTensor`):
+ Mapping between atoms in the atom37 and atom14 representations.
+ atom37_atom_exists (`torch.FloatTensor`):
+ Whether each atom exists in the atom37 representation.
+ residue_index (`torch.FloatTensor`):
+ The index of each residue in the protein chain. Unless internal padding tokens are used, this will just be
+ a sequence of integers from 0 to `sequence_length`.
+ lddt_head (`torch.FloatTensor`):
+ Raw outputs from the lddt head used to compute plddt.
+ plddt (`torch.FloatTensor`):
+ Per-residue confidence scores. Regions of low confidence may indicate areas where the model's prediction is
+ uncertain, or where the protein structure is disordered.
+ ptm_logits (`torch.FloatTensor`):
+ Raw logits used for computing ptm.
+ ptm (`torch.FloatTensor`):
+ TM-score output representing the model's high-level confidence in the overall structure.
+ aligned_confidence_probs (`torch.FloatTensor`):
+ Per-residue confidence scores for the aligned structure.
+ predicted_aligned_error (`torch.FloatTensor`):
+ Predicted error between the model's prediction and the ground truth.
+ max_predicted_aligned_error (`torch.FloatTensor`):
+ Per-sample maximum predicted error.
+ """
+
+ frames: torch.FloatTensor = None
+ sidechain_frames: torch.FloatTensor = None
+ unnormalized_angles: torch.FloatTensor = None
+ angles: torch.FloatTensor = None
+ positions: torch.FloatTensor = None
+ states: torch.FloatTensor = None
+ s_s: torch.FloatTensor = None
+ s_z: torch.FloatTensor = None
+ distogram_logits: torch.FloatTensor = None
+ lm_logits: torch.FloatTensor = None
+ aatype: torch.FloatTensor = None
+ atom14_atom_exists: torch.FloatTensor = None
+ residx_atom14_to_atom37: torch.FloatTensor = None
+ residx_atom37_to_atom14: torch.FloatTensor = None
+ atom37_atom_exists: torch.FloatTensor = None
+ residue_index: torch.FloatTensor = None
+ lddt_head: torch.FloatTensor = None
+ plddt: torch.FloatTensor = None
+ ptm_logits: torch.FloatTensor = None
+ ptm: torch.FloatTensor = None
+ aligned_confidence_probs: torch.FloatTensor = None
+ predicted_aligned_error: torch.FloatTensor = None
+ max_predicted_aligned_error: torch.FloatTensor = None
+
+
+ESMFOLD_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ masking_pattern (`torch.LongTensor` of shape `({0})`, *optional*):
+ Locations of tokens to mask during training as a form of regularization. Mask values selected in `[0, 1]`.
+ num_recycles (`int`, *optional*, defaults to `None`):
+ Number of times to recycle the input sequence. If `None`, defaults to `config.num_recycles`. "Recycling"
+ consists of passing the output of the folding trunk back in as input to the trunk. During training, the
+ number of recycles should vary with each batch, to ensure that the model learns to output valid predictions
+ after each recycle. During inference, num_recycles should be set to the highest value that the model was
+ trained with for maximum accuracy. Accordingly, when this value is set to `None`, config.max_recycles is
+ used.
+"""
+
+
+def is_fp16_enabled():
+ # Autocast world
+ fp16_enabled = torch.get_autocast_gpu_dtype() == torch.float16
+ fp16_enabled = fp16_enabled and torch.is_autocast_enabled()
+
+ return fp16_enabled
+
+
+def is_deepspeed_initialized():
+ if is_deepspeed_available():
+ return False
+ else:
+ try:
+ import deepspeed
+
+ # This is not available in all DeepSpeed versions.
+ return deepspeed.utils.is_initialized()
+ except Exception:
+ return False
+
+
+def collate_dense_tensors(samples: List[torch.Tensor], pad_v: float = 0) -> torch.Tensor:
+ """
+ Takes a list of tensors with the following dimensions:
+ [(d_11, ..., d_1K),
+ (d_21, ..., d_2K), ..., (d_N1, ..., d_NK)]
+ and stack + pads them into a single tensor of:
+ (N, max_i=1,N { d_i1 }, ..., max_i=1,N {diK})
+ """
+ if len(samples) == 0:
+ return torch.Tensor()
+ if len({x.dim() for x in samples}) != 1:
+ raise RuntimeError(f"Samples has varying dimensions: {[x.dim() for x in samples]}")
+ (device,) = tuple({x.device for x in samples}) # assumes all on same device
+ max_shape = [max(lst) for lst in zip(*[x.shape for x in samples])]
+ result = torch.empty(len(samples), *max_shape, dtype=samples[0].dtype, device=device)
+ result.fill_(pad_v)
+ for i in range(len(samples)):
+ result_i = result[i]
+ t = samples[i]
+ result_i[tuple(slice(0, k) for k in t.shape)] = t
+ return result
+
+
+def flatten_final_dims(t: torch.Tensor, no_dims: int):
+ return t.reshape(t.shape[:-no_dims] + (-1,))
+
+
+def permute_final_dims(tensor: torch.Tensor, inds: List[int]):
+ zero_index = -1 * len(inds)
+ first_inds = list(range(len(tensor.shape[:zero_index])))
+ return tensor.permute(first_inds + [zero_index + i for i in inds])
+
+
+def dict_multimap(fn, dicts):
+ first = dicts[0]
+ new_dict = {}
+ for k, v in first.items():
+ all_v = [d[k] for d in dicts]
+ if type(v) is dict:
+ new_dict[k] = dict_multimap(fn, all_v)
+ else:
+ new_dict[k] = fn(all_v)
+
+ return new_dict
+
+
+def trunc_normal_init_(weights, scale=1.0, fan="fan_in"):
+ shape = weights.shape
+ scale = scale / max(1, shape[1])
+
+ if not is_scipy_available():
+ logger.warning(
+ "This init requires scipy, but scipy was not found, default to an approximation that might not be"
+ " equivalent."
+ )
+ std = math.sqrt(scale)
+ torch.nn.init.normal_(weights, std=std).clamp(min=0.0, max=2.0 * std)
+
+ else:
+ from scipy.stats import truncnorm
+
+ std = math.sqrt(scale) / truncnorm.std(a=-2, b=2, loc=0, scale=1)
+ samples = truncnorm.rvs(a=-2, b=2, loc=0, scale=std, size=weights.numel())
+ samples = np.reshape(samples, shape)
+ weights.copy_(torch.tensor(samples, device=weights.device))
+
+
+def ipa_point_weights_init_(weights):
+ with torch.no_grad():
+ softplus_inverse_1 = 0.541324854612918
+ weights.fill_(softplus_inverse_1)
+
+
+class EsmFoldLinear(nn.Linear):
+ """
+ A Linear layer with built-in nonstandard initializations. Called just like torch.nn.Linear.
+
+ Implements the initializers in 1.11.4, plus some additional ones found in the code.
+ """
+
+ def __init__(
+ self,
+ in_dim: int,
+ out_dim: int,
+ bias: bool = True,
+ init: str = "default",
+ init_fn: Optional[Callable[[torch.Tensor, torch.Tensor], None]] = None,
+ ):
+ """
+ Args:
+ in_dim:
+ The final dimension of inputs to the layer
+ out_dim:
+ The final dimension of layer outputs
+ bias:
+ Whether to learn an additive bias. True by default
+ init:
+ The initializer to use. Choose from:
+
+ "default": LeCun fan-in truncated normal initialization "relu": He initialization w/ truncated normal
+ distribution "glorot": Fan-average Glorot uniform initialization "gating": Weights=0, Bias=1 "normal":
+ Normal initialization with std=1/sqrt(fan_in) "final": Weights=0, Bias=0
+
+ Overridden by init_fn if the latter is not None.
+ init_fn:
+ A custom initializer taking weight and bias as inputs. Overrides init if not None.
+ """
+ super().__init__(in_dim, out_dim, bias=bias)
+
+ if bias:
+ with torch.no_grad():
+ self.bias.fill_(0)
+ self.init = init
+ self.init_fn = init_fn
+
+ if init not in ["default", "relu", "glorot", "gating", "normal", "final"]:
+ raise ValueError("Invalid init string.")
+
+
+class EsmFoldLayerNorm(nn.Module):
+ def __init__(self, c_in, eps=1e-5):
+ super().__init__()
+
+ self.c_in = (c_in,)
+ self.eps = eps
+
+ self.weight = nn.Parameter(torch.ones(c_in))
+ self.bias = nn.Parameter(torch.zeros(c_in))
+
+ def forward(self, x):
+ d = x.dtype
+ if d is torch.bfloat16 and not is_deepspeed_initialized():
+ with torch.cuda.amp.autocast(enabled=False):
+ out = nn.functional.layer_norm(x, self.c_in, self.weight.to(dtype=d), self.bias.to(dtype=d), self.eps)
+ else:
+ out = nn.functional.layer_norm(x, self.c_in, self.weight, self.bias, self.eps)
+
+ return out
+
+
+@torch.jit.ignore
+def softmax_no_cast(t: torch.Tensor, dim: int = -1) -> torch.Tensor:
+ """
+ Softmax, but without automatic casting to fp32 when the input is of type bfloat16
+ """
+ d = t.dtype
+ if d is torch.bfloat16 and not is_deepspeed_initialized():
+ with torch.cuda.amp.autocast(enabled=False):
+ s = torch.nn.functional.softmax(t, dim=dim)
+ else:
+ s = torch.nn.functional.softmax(t, dim=dim)
+
+ return s
+
+
+class EsmFoldAttention(nn.Module):
+ """
+ Standard multi-head attention using AlphaFold's default layer initialization. Allows multiple bias vectors.
+ """
+
+ def __init__(
+ self,
+ c_q: int,
+ c_k: int,
+ c_v: int,
+ c_hidden: int,
+ no_heads: int,
+ gating: bool = True,
+ ):
+ """
+ Args:
+ c_q:
+ Input dimension of query data
+ c_k:
+ Input dimension of key data
+ c_v:
+ Input dimension of value data
+ c_hidden:
+ Per-head hidden dimension
+ no_heads:
+ Number of attention heads
+ gating:
+ Whether the output should be gated using query data
+ """
+ super().__init__()
+
+ self.c_q = c_q
+ self.c_k = c_k
+ self.c_v = c_v
+ self.c_hidden = c_hidden
+ self.no_heads = no_heads
+ self.gating = gating
+
+ # DISCREPANCY: c_hidden is not the per-head channel dimension, as
+ # stated in the supplement, but the overall channel dimension.
+
+ self.linear_q = EsmFoldLinear(self.c_q, self.c_hidden * self.no_heads, bias=False, init="glorot")
+ self.linear_k = EsmFoldLinear(self.c_k, self.c_hidden * self.no_heads, bias=False, init="glorot")
+ self.linear_v = EsmFoldLinear(self.c_v, self.c_hidden * self.no_heads, bias=False, init="glorot")
+ self.linear_o = EsmFoldLinear(self.c_hidden * self.no_heads, self.c_q, init="final")
+
+ self.linear_g = None
+ if self.gating:
+ self.linear_g = EsmFoldLinear(self.c_q, self.c_hidden * self.no_heads, init="gating")
+
+ self.sigmoid = nn.Sigmoid()
+
+ def _prep_qkv(self, q_x: torch.Tensor, kv_x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ # [*, Q/K/V, H * C_hidden]
+ q = self.linear_q(q_x)
+ k = self.linear_k(kv_x)
+ v = self.linear_v(kv_x)
+
+ # [*, Q/K, H, C_hidden]
+ q = q.view(q.shape[:-1] + (self.no_heads, -1))
+ k = k.view(k.shape[:-1] + (self.no_heads, -1))
+ v = v.view(v.shape[:-1] + (self.no_heads, -1))
+
+ # [*, H, Q/K, C_hidden]
+ q = q.transpose(-2, -3)
+ k = k.transpose(-2, -3)
+ v = v.transpose(-2, -3)
+
+ q /= math.sqrt(self.c_hidden)
+
+ return q, k, v
+
+ def _wrap_up(self, o: torch.Tensor, q_x: torch.Tensor) -> torch.Tensor:
+ if self.linear_g is not None:
+ g = self.sigmoid(self.linear_g(q_x))
+
+ # [*, Q, H, C_hidden]
+ g = g.view(g.shape[:-1] + (self.no_heads, -1))
+ o = o * g
+
+ # [*, Q, H * C_hidden]
+ o = flatten_final_dims(o, 2)
+
+ # [*, Q, C_q]
+ o = self.linear_o(o)
+
+ return o
+
+ def forward(
+ self,
+ q_x: torch.Tensor,
+ kv_x: torch.Tensor,
+ biases: Optional[List[torch.Tensor]] = None,
+ use_memory_efficient_kernel: bool = False,
+ use_lma: bool = False,
+ lma_q_chunk_size: int = 1024,
+ lma_kv_chunk_size: int = 4096,
+ use_flash: bool = False,
+ flash_mask: Optional[torch.Tensor] = None,
+ ) -> torch.Tensor:
+ """
+ Args:
+ q_x:
+ [*, Q, C_q] query data
+ kv_x:
+ [*, K, C_k] key data
+ biases:
+ List of biases that broadcast to [*, H, Q, K]
+ use_memory_efficient_kernel:
+ Whether to use a custom memory-efficient attention kernel. This should be the default choice for most.
+ If none of the "use_<...>" flags are True, a stock PyTorch implementation is used instead
+ use_lma:
+ Whether to use low-memory attention (Staats & Rabe 2021). If none of the "use_<...>" flags are True, a
+ stock PyTorch implementation is used instead
+ lma_q_chunk_size:
+ Query chunk size (for LMA)
+ lma_kv_chunk_size:
+ Key/Value chunk size (for LMA)
+ Returns
+ [*, Q, C_q] attention update
+ """
+ if use_lma and (lma_q_chunk_size is None or lma_kv_chunk_size is None):
+ raise ValueError("If use_lma is specified, lma_q_chunk_size and lma_kv_chunk_size must be provided")
+
+ if use_flash and biases is not None:
+ raise ValueError("use_flash is incompatible with the bias option. For masking, use flash_mask instead")
+
+ attn_options = [use_memory_efficient_kernel, use_lma, use_flash]
+ if sum(attn_options) > 1:
+ raise ValueError("Choose at most one alternative attention algorithm")
+
+ if biases is None:
+ biases = []
+
+ # [*, H, Q/K, C_hidden]
+ query, key, value = self._prep_qkv(q_x, kv_x)
+ key = permute_final_dims(key, (1, 0))
+
+ # [*, H, Q, K]
+ output = torch.matmul(query, key)
+ for b in biases:
+ output += b
+ output = softmax_no_cast(output, -1)
+
+ # [*, H, Q, C_hidden]
+ output = torch.matmul(output, value)
+ output = output.transpose(-2, -3)
+ output = self._wrap_up(output, q_x)
+
+ return output
+
+
+class EsmFoldTriangleAttention(nn.Module):
+ def __init__(self, c_in, c_hidden, no_heads, starting=True, inf=1e9):
+ """
+ Args:
+ c_in:
+ Input channel dimension
+ c_hidden:
+ Overall hidden channel dimension (not per-head)
+ no_heads:
+ Number of attention heads
+ """
+ super().__init__()
+
+ self.c_in = c_in
+ self.c_hidden = c_hidden
+ self.no_heads = no_heads
+ self.starting = starting
+ self.inf = inf
+
+ self.layer_norm = LayerNorm(self.c_in)
+
+ self.linear = EsmFoldLinear(c_in, self.no_heads, bias=False, init="normal")
+
+ self.mha = EsmFoldAttention(self.c_in, self.c_in, self.c_in, self.c_hidden, self.no_heads)
+
+ @torch.jit.ignore
+ def _chunk(
+ self,
+ x: torch.Tensor,
+ biases: List[torch.Tensor],
+ chunk_size: int,
+ use_memory_efficient_kernel: bool = False,
+ use_lma: bool = False,
+ inplace_safe: bool = False,
+ ) -> torch.Tensor:
+ "triangle! triangle!"
+ mha_inputs = {
+ "q_x": x,
+ "kv_x": x,
+ "biases": biases,
+ }
+
+ return chunk_layer(
+ partial(self.mha, use_memory_efficient_kernel=use_memory_efficient_kernel, use_lma=use_lma),
+ mha_inputs,
+ chunk_size=chunk_size,
+ no_batch_dims=len(x.shape[:-2]),
+ _out=x if inplace_safe else None,
+ )
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ mask: Optional[torch.Tensor] = None,
+ chunk_size: Optional[int] = None,
+ use_memory_efficient_kernel: bool = False,
+ use_lma: bool = False,
+ inplace_safe: bool = False,
+ ) -> torch.Tensor:
+ """
+ Args:
+ x:
+ [*, I, J, C_in] input tensor (e.g. the pair representation)
+ Returns:
+ [*, I, J, C_in] output tensor
+ """
+ if mask is None:
+ # [*, I, J]
+ mask = x.new_ones(
+ x.shape[:-1],
+ )
+
+ if not self.starting:
+ x = x.transpose(-2, -3)
+ mask = mask.transpose(-1, -2)
+
+ # [*, I, J, C_in]
+ x = self.layer_norm(x)
+
+ # [*, I, 1, 1, J]
+ mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]
+
+ # [*, H, I, J]
+ triangle_bias = permute_final_dims(self.linear(x), (2, 0, 1))
+
+ # [*, 1, H, I, J]
+ triangle_bias = triangle_bias.unsqueeze(-4)
+
+ biases = [mask_bias, triangle_bias]
+
+ if chunk_size is not None:
+ x = self._chunk(
+ x,
+ biases,
+ chunk_size,
+ use_memory_efficient_kernel=use_memory_efficient_kernel,
+ use_lma=use_lma,
+ inplace_safe=inplace_safe,
+ )
+ else:
+ x = self.mha(
+ q_x=x, kv_x=x, biases=biases, use_memory_efficient_kernel=use_memory_efficient_kernel, use_lma=use_lma
+ )
+
+ if not self.starting:
+ x = x.transpose(-2, -3)
+
+ return x
+
+
+class EsmFoldTriangleMultiplicativeUpdate(nn.Module):
+ """
+ Implements Algorithms 11 and 12.
+ """
+
+ def __init__(self, config, _outgoing=True):
+ super().__init__()
+ c_hidden = config.pairwise_state_dim
+ self._outgoing = _outgoing
+
+ self.linear_a_p = EsmFoldLinear(c_hidden, c_hidden)
+ self.linear_a_g = EsmFoldLinear(c_hidden, c_hidden, init="gating")
+ self.linear_b_p = EsmFoldLinear(c_hidden, c_hidden)
+ self.linear_b_g = EsmFoldLinear(c_hidden, c_hidden, init="gating")
+ self.linear_g = EsmFoldLinear(c_hidden, c_hidden, init="gating")
+ self.linear_z = EsmFoldLinear(c_hidden, c_hidden, init="final")
+
+ self.layer_norm_in = LayerNorm(c_hidden)
+ self.layer_norm_out = LayerNorm(c_hidden)
+
+ self.sigmoid = nn.Sigmoid()
+
+ def _combine_projections(
+ self, a: torch.Tensor, b: torch.Tensor, _inplace_chunk_size: Optional[int] = None
+ ) -> torch.Tensor:
+ if self._outgoing:
+ a = permute_final_dims(a, (2, 0, 1))
+ b = permute_final_dims(b, (2, 1, 0))
+ else:
+ a = permute_final_dims(a, (2, 1, 0))
+ b = permute_final_dims(b, (2, 0, 1))
+
+ if _inplace_chunk_size is not None:
+ # To be replaced by torch vmap
+ for i in range(0, a.shape[-3], _inplace_chunk_size):
+ a_chunk = a[..., i : i + _inplace_chunk_size, :, :]
+ b_chunk = b[..., i : i + _inplace_chunk_size, :, :]
+ a[..., i : i + _inplace_chunk_size, :, :] = torch.matmul(
+ a_chunk,
+ b_chunk,
+ )
+
+ p = a
+ else:
+ p = torch.matmul(a, b)
+
+ return permute_final_dims(p, (1, 2, 0))
+
+ def _inference_forward(
+ self,
+ z: torch.Tensor,
+ mask: Optional[torch.Tensor] = None,
+ inplace_chunk_size: Optional[int] = None,
+ with_add: bool = True,
+ ):
+ """
+ Args:
+ z:
+ A [*, N, N, C_z] pair representation
+ mask:
+ A [*, N, N] pair mask
+ inplace_chunk_size:
+ Size of chunks used in the main computation. Increase to trade memory for speed.
+ with_add:
+ If True, z is overwritten with (z + update). Otherwise, it is overwritten with (update).
+ Returns:
+ A reference to the overwritten z
+
+ More memory-efficient, inference-only version of the forward function. Uses in-place operations, fusion of the
+ addition that happens after this module in the Evoformer, a smidge of recomputation, and a cache of overwritten
+ values to lower peak memory consumption of this module from 5x the size of the input tensor z to 2.5x its size.
+ Useful for inference on extremely long sequences.
+
+ It works as follows. We will make reference to variables used in the default forward implementation below.
+ Naively, triangle multiplication attention requires the manifestation of 5 tensors the size of z: 1) z, the
+ "square" input tensor, 2) a, the first projection of z, 3) b, the second projection of b, 4) g, a z-sized mask,
+ and 5) a z-sized tensor for intermediate computations. For large N, this is prohibitively expensive; for
+ N=4000, for example, z is more than 8GB alone. To avoid this problem, we compute b, g, and all intermediate
+ tensors in small chunks, noting that the chunks required to compute a chunk of the output depend only on the
+ tensor a and corresponding vertical and horizontal chunks of z. This suggests an algorithm that loops over
+ pairs of chunks of z: hereafter "columns" and "rows" of z, even though each "column" and "row" in fact contains
+ inplace_chunk_size contiguous true columns and rows of z. Writing output chunks to a new tensor would bring
+ total memory consumption down to 3x the size of z. However, more memory can be saved by writing output chunks
+ directly to z in-place. WLOG, we choose to write output chunks vertically, overwriting the ith "column" of z at
+ the end of the ith iteration of the main loop. Despite this overwriting, the ith column is always one column
+ ahead of previously overwritten columns and can be recovered directly from z. After the first iteration,
+ however, the ith row of z is always at least partially overwritten. For this reason, we introduce the z-cache,
+ a tensor one-half the size of z. The z-cache initially contains the left half (2nd and 3rd quadrants) of z. For
+ 0 < i < N/2, the missing left part of the ith row of z is recovered from this cache at the beginning of the ith
+ iteration. Once i exceeds n/2, the cache is "reoriented" to encompass the 3rd and 4th quadrants of z instead.
+ Though the 3rd quadrant of the original z is entirely overwritten at this point, it can be recovered from the
+ z-cache itself. Thereafter, the ith row of z can be recovered in its entirety from the reoriented z-cache.
+ After the final iteration, z has been completely overwritten and contains the triangular multiplicative update.
+ If with_add is True, it instead contains the sum of z and the triangular multiplicative update. In either case,
+ peak memory consumption is just 2.5x the size of z, disregarding memory used for chunks and other small
+ variables.
+ """
+ if mask is None:
+ mask = z.new_ones(z.shape[:-1])
+
+ mask = mask.unsqueeze(-1)
+
+ def compute_projection_helper(pair, mask, a=True):
+ if a:
+ linear_g = self.linear_a_g
+ linear_p = self.linear_a_p
+ else:
+ linear_g = self.linear_b_g
+ linear_p = self.linear_b_p
+
+ pair = self.layer_norm_in(pair)
+ p = linear_g(pair)
+ p.sigmoid_()
+ p *= linear_p(pair)
+ p *= mask
+ p = permute_final_dims(p, (2, 0, 1))
+ return p
+
+ def compute_projection(pair, mask, a=True, chunked=True):
+ need_transpose = self._outgoing ^ a
+ if not chunked:
+ p = compute_projection_helper(pair, mask, a)
+ if need_transpose:
+ p = p.transpose(-1, -2)
+ else:
+ # This computation is chunked so as not to exceed our 2.5x
+ # budget with a large intermediate tensor
+ linear_g = self.linear_a_g if a else self.linear_b_g
+ c = linear_g.bias.shape[-1]
+ out_shape = pair.shape[:-3] + (c,) + pair.shape[-3:-1]
+ p = pair.new_zeros(out_shape)
+ for i in range(0, pair.shape[-3], inplace_chunk_size):
+ pair_chunk = pair[..., i : i + inplace_chunk_size, :, :]
+ pair_chunk = compute_projection_helper(
+ pair[..., i : i + inplace_chunk_size, :, :],
+ mask[..., i : i + inplace_chunk_size, :, :],
+ a,
+ )
+ if need_transpose:
+ pair_chunk = pair_chunk.transpose(-1, -2)
+ p[..., i : i + inplace_chunk_size] = pair_chunk
+ else:
+ p[..., i : i + inplace_chunk_size, :] = pair_chunk
+
+ del pair_chunk
+
+ return p
+
+ # We start by fully manifesting a. In addition to the input, this
+ # brings total memory consumption to 2x z (disregarding size of chunks)
+ # [*, N, N, c]
+ a = compute_projection(z, mask, True, chunked=True)
+
+ if inplace_chunk_size is not None:
+ n = a.shape[-1]
+ half_n = n // 2 + n % 2
+ row_dim = -3
+ col_dim = -2
+ b_chunk_dim = row_dim if self._outgoing else col_dim
+
+ def empty_slicer(t):
+ return [slice(None) for _ in t.shape]
+
+ def slice_tensor(t, start, end, dim):
+ # Slices start:end from the dim dimension of t
+ s = empty_slicer(t)
+ s[dim] = slice(start, end)
+ return t[s]
+
+ def flip_z_cache_(z_cache, z):
+ # "Reorient" the z_cache (see below), filling it with quadrants
+ # 3---recovered from the z_cache---and 4---recovered from z---
+ # of the input tensor z.
+ quadrant_3 = slice_tensor(z_cache, half_n, None, row_dim)
+ z_cache = z_cache.transpose(row_dim, col_dim)
+
+ # If n is odd, we need to shrink the z_cache by one row
+ z_cache = z_cache[..., : (n // 2), :, :]
+
+ # Move the 3rd quadrant of z into the
+ first_half_slicer = empty_slicer(z_cache)
+ first_half_slicer[col_dim] = slice(0, half_n)
+ z_cache[first_half_slicer] = quadrant_3
+
+ # Get the fourth quadrant of z
+ quadrant_4 = slice_tensor(z, half_n, None, row_dim)
+ quadrant_4 = slice_tensor(quadrant_4, half_n, None, col_dim)
+
+ # Insert said quadrant into the rotated z-cache
+ quadrant_3_slicer = empty_slicer(z_cache)
+ quadrant_3_slicer[col_dim] = slice(half_n, None)
+
+ z_cache[quadrant_3_slicer] = quadrant_4
+
+ return z_cache
+
+ # Initialize the z cache to the left half of z.
+ z_cache_shape = list(z.shape)
+ z_cache_shape[col_dim] = half_n
+ z_cache = z.new_zeros(z_cache_shape)
+ z_cache_slicer = empty_slicer(z_cache)
+ z_cache_slicer[col_dim] = slice(0, half_n)
+ z_cache.copy_(z[z_cache_slicer])
+ z_cache_rotated = False
+
+ # We need to reorient the z-cache at the halfway point, and we
+ # don't want a single chunk to straddle that point. We contract one
+ # of the chunks in the middle to address that problem.
+ i_range = list(range(0, half_n, inplace_chunk_size))
+ initial_offsets = [i_2 - i_1 for i_1, i_2 in zip(i_range, i_range[1:] + [half_n])]
+ after_half = list(range(half_n, n, inplace_chunk_size))
+ after_half_offsets = [inplace_chunk_size for _ in after_half]
+ combined_range_with_offsets = zip(i_range + after_half, initial_offsets + after_half_offsets)
+ for i, offset in combined_range_with_offsets:
+ if not z_cache_rotated and i >= half_n:
+ z_cache = flip_z_cache_(z_cache, z)
+ z_cache_rotated = True
+
+ z_chunk_b = slice_tensor(z, i, i + offset, b_chunk_dim)
+ mask_chunk = slice_tensor(mask, i, i + offset, b_chunk_dim)
+
+ z_chunk_b = z_chunk_b.clone()
+ if b_chunk_dim == col_dim:
+ z_chunk_b = slice_tensor(z, i, i + offset, col_dim)
+ else: # b_chunk_dim == row_dim
+ # In this case, the b-dimension (b_chunk_dim) is partially
+ # overwritten at the end of each iteration. We need to
+ # restore the missing component from the z-cache.
+ if not z_cache_rotated:
+ z_chunk_slicer = empty_slicer(z_chunk_b)
+ z_chunk_slicer[col_dim] = slice(0, half_n)
+ z_chunk_b[z_chunk_slicer] = slice_tensor(z_cache, i, i + offset, row_dim)
+ else:
+ z_cache_offset = i - half_n
+ z_chunk_b = slice_tensor(z_cache, z_cache_offset, z_cache_offset + offset, row_dim)
+
+ b_chunk = compute_projection(z_chunk_b, mask_chunk, a=False, chunked=False)
+ del z_chunk_b
+
+ x_chunk = torch.matmul(a, b_chunk)
+ x_chunk = permute_final_dims(x_chunk, (1, 2, 0))
+ x_chunk = self.layer_norm_out(x_chunk)
+ x_chunk = self.linear_z(x_chunk)
+
+ # The g dimension (col_dim) is parallel to and ahead of the
+ # overwrites in z. We can extract the g chunk normally.
+ z_chunk_g = slice_tensor(z, i, i + offset, col_dim)
+ g_chunk = self.linear_g(self.layer_norm_in(z_chunk_g))
+ g_chunk.sigmoid_()
+ del z_chunk_g
+
+ x_chunk *= g_chunk
+
+ # Write the columns into z in-place
+ z_slicer = empty_slicer(z)
+ z_slicer[col_dim] = slice(i, i + offset)
+ if with_add:
+ z[z_slicer] += x_chunk
+ else:
+ z[z_slicer] = x_chunk
+ else:
+ b = compute_projection(z, mask, False, False)
+ x = torch.matmul(a, b)
+ x = self.layer_norm_out(x)
+ x = self.linear_z(x)
+ g = self.linear_g(z)
+ g.sigmoid_()
+ x *= g
+ if with_add:
+ z += x
+ else:
+ z = x
+
+ return z
+
+ def forward(
+ self,
+ z: torch.Tensor,
+ mask: Optional[torch.Tensor] = None,
+ inplace_safe: bool = False,
+ _add_with_inplace: bool = False,
+ _inplace_chunk_size: Optional[int] = 256,
+ ) -> torch.Tensor:
+ """
+ Args:
+ x:
+ [*, N_res, N_res, C_z] input tensor
+ mask:
+ [*, N_res, N_res] input mask
+ Returns:
+ [*, N_res, N_res, C_z] output tensor
+ """
+ if inplace_safe:
+ x = self._inference_forward(
+ z,
+ mask,
+ inplace_chunk_size=_inplace_chunk_size,
+ with_add=_add_with_inplace,
+ )
+ return x
+
+ if mask is None:
+ mask = z.new_ones(z.shape[:-1])
+
+ mask = mask.unsqueeze(-1)
+
+ z = self.layer_norm_in(z)
+ a = mask
+ a = a * self.sigmoid(self.linear_a_g(z))
+ a = a * self.linear_a_p(z)
+ b = mask
+ b = b * self.sigmoid(self.linear_b_g(z))
+ b = b * self.linear_b_p(z)
+
+ if is_fp16_enabled():
+ with torch.cuda.amp.autocast(enabled=False):
+ x = self._combine_projections(a.float(), b.float())
+ else:
+ x = self._combine_projections(a, b)
+
+ del a, b
+ x = self.layer_norm_out(x)
+ x = self.linear_z(x)
+ g = self.sigmoid(self.linear_g(z))
+ x = x * g
+
+ return x
+
+
+class EsmFoldPreTrainedModel(EsmPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ # Subclass `EsMPreTrainedModel` to deal with special init
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, EsmFoldLinear):
+ with torch.no_grad():
+ if module.init_fn is not None:
+ module.init_fn(module.weight, module.bias)
+ elif module.init == "default":
+ trunc_normal_init_(module.weight, scale=1.0)
+ elif module.init == "relu":
+ trunc_normal_init_(module.weight, scale=2.0)
+ elif module.init == "glorot":
+ nn.init.xavier_uniform_(module.weight, gain=1)
+ elif module.init == "gating":
+ module.weight.fill_(0.0)
+ if module.bias:
+ module.bias.fill_(1.0)
+ elif module.init == "normal":
+ torch.nn.init.kaiming_normal_(module.weight, nonlinearity="linear")
+ elif module.init == "final":
+ module.weight.fill_(0.0)
+ elif isinstance(module, EsmFoldInvariantPointAttention):
+ ipa_point_weights_init_(module.head_weights)
+ elif isinstance(module, EsmFoldTriangularSelfAttentionBlock):
+ torch.nn.init.zeros_(module.tri_mul_in.linear_z.weight)
+ torch.nn.init.zeros_(module.tri_mul_in.linear_z.bias)
+ torch.nn.init.zeros_(module.tri_mul_out.linear_z.weight)
+ torch.nn.init.zeros_(module.tri_mul_out.linear_z.bias)
+ torch.nn.init.zeros_(module.tri_att_start.mha.linear_o.weight)
+ torch.nn.init.zeros_(module.tri_att_start.mha.linear_o.bias)
+ torch.nn.init.zeros_(module.tri_att_end.mha.linear_o.weight)
+ torch.nn.init.zeros_(module.tri_att_end.mha.linear_o.bias)
+
+ torch.nn.init.zeros_(module.sequence_to_pair.o_proj.weight)
+ torch.nn.init.zeros_(module.sequence_to_pair.o_proj.bias)
+ torch.nn.init.zeros_(module.pair_to_sequence.linear.weight)
+ torch.nn.init.zeros_(module.seq_attention.o_proj.weight)
+ torch.nn.init.zeros_(module.seq_attention.o_proj.bias)
+ torch.nn.init.zeros_(module.mlp_seq.mlp[-2].weight)
+ torch.nn.init.zeros_(module.mlp_seq.mlp[-2].bias)
+ torch.nn.init.zeros_(module.mlp_pair.mlp[-2].weight)
+ torch.nn.init.zeros_(module.mlp_pair.mlp[-2].bias)
+ else:
+ super()._init_weights(module)
+
+
+class EsmFoldSelfAttention(nn.Module):
+ def __init__(self, embed_dim, num_heads, head_width, gated=False):
+ super().__init__()
+ assert embed_dim == num_heads * head_width
+
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.head_width = head_width
+
+ self.proj = nn.Linear(embed_dim, embed_dim * 3, bias=False)
+ self.o_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+ self.gated = gated
+ if gated:
+ self.g_proj = nn.Linear(embed_dim, embed_dim)
+ torch.nn.init.zeros_(self.g_proj.weight)
+ torch.nn.init.ones_(self.g_proj.bias)
+
+ self.rescale_factor = self.head_width**-0.5
+
+ torch.nn.init.zeros_(self.o_proj.bias)
+
+ def forward(self, x, mask=None, bias=None, indices=None):
+ """
+ Basic self attention with optional mask and external pairwise bias. To handle sequences of different lengths,
+ use mask.
+
+ Inputs:
+ x: batch of input sequneces (.. x L x C) mask: batch of boolean masks where 1=valid, 0=padding position (..
+ x L_k) bias: batch of scalar pairwise attention biases (.. x Lq x Lk x num_heads)
+
+ Outputs:
+ sequence projection (B x L x embed_dim), attention maps (B x L x L x num_heads)
+ """
+
+ t = self.proj(x).view(*x.shape[:2], self.num_heads, -1)
+ t = t.permute(0, 2, 1, 3)
+ q, k, v = t.chunk(3, dim=-1)
+
+ q = self.rescale_factor * q
+ a = torch.einsum("...qc,...kc->...qk", q, k)
+
+ # Add external attention bias.
+ if bias is not None:
+ a = a + bias.permute(0, 3, 1, 2)
+
+ # Do not attend to padding tokens.
+ if mask is not None:
+ mask = mask[:, None, None]
+ a = a.masked_fill(mask == False, -np.inf) # noqa: E712
+
+ a = nn.functional.softmax(a, dim=-1)
+
+ y = torch.einsum("...hqk,...hkc->...qhc", a, v)
+ y = y.reshape(*y.shape[:2], -1)
+
+ if self.gated:
+ y = self.g_proj(x).sigmoid() * y
+ y = self.o_proj(y)
+
+ return y, a.permute(0, 3, 1, 2)
+
+
+class EsmFoldDropout(nn.Module):
+ """
+ Implementation of dropout with the ability to share the dropout mask along a particular dimension.
+ """
+
+ def __init__(self, r: float, batch_dim: Union[int, List[int]]):
+ super().__init__()
+
+ self.r = r
+ if type(batch_dim) == int:
+ batch_dim = [batch_dim]
+ self.batch_dim = batch_dim
+ self.dropout = nn.Dropout(self.r)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ shape = list(x.shape)
+ if self.batch_dim is not None:
+ for bd in self.batch_dim:
+ shape[bd] = 1
+ return x * self.dropout(x.new_ones(shape))
+
+
+class EsmFoldSequenceToPair(nn.Module):
+ def __init__(self, sequence_state_dim, inner_dim, pairwise_state_dim):
+ super().__init__()
+
+ self.layernorm = nn.LayerNorm(sequence_state_dim)
+ self.proj = nn.Linear(sequence_state_dim, inner_dim * 2, bias=True)
+ self.o_proj = nn.Linear(2 * inner_dim, pairwise_state_dim, bias=True)
+
+ torch.nn.init.zeros_(self.proj.bias)
+ torch.nn.init.zeros_(self.o_proj.bias)
+
+ def forward(self, sequence_state):
+ """
+ Inputs:
+ sequence_state: B x L x sequence_state_dim
+
+ Output:
+ pairwise_state: B x L x L x pairwise_state_dim
+
+ Intermediate state:
+ B x L x L x 2*inner_dim
+ """
+
+ assert len(sequence_state.shape) == 3
+
+ s = self.layernorm(sequence_state)
+ s = self.proj(s)
+ q, k = s.chunk(2, dim=-1)
+
+ prod = q[:, None, :, :] * k[:, :, None, :]
+ diff = q[:, None, :, :] - k[:, :, None, :]
+
+ x = torch.cat([prod, diff], dim=-1)
+ x = self.o_proj(x)
+
+ return x
+
+
+class EsmFoldPairToSequence(nn.Module):
+ def __init__(self, pairwise_state_dim, num_heads):
+ super().__init__()
+
+ self.layernorm = nn.LayerNorm(pairwise_state_dim)
+ self.linear = nn.Linear(pairwise_state_dim, num_heads, bias=False)
+
+ def forward(self, pairwise_state):
+ """
+ Inputs:
+ pairwise_state: B x L x L x pairwise_state_dim
+
+ Output:
+ pairwise_bias: B x L x L x num_heads
+ """
+ assert len(pairwise_state.shape) == 4
+ z = self.layernorm(pairwise_state)
+ pairwise_bias = self.linear(z)
+ return pairwise_bias
+
+
+class EsmFoldResidueMLP(nn.Module):
+ def __init__(self, embed_dim, inner_dim, dropout=0):
+ super().__init__()
+
+ self.mlp = nn.Sequential(
+ nn.LayerNorm(embed_dim),
+ nn.Linear(embed_dim, inner_dim),
+ nn.ReLU(),
+ nn.Linear(inner_dim, embed_dim),
+ nn.Dropout(dropout),
+ )
+
+ def forward(self, x):
+ return x + self.mlp(x)
+
+
+class EsmFoldTriangularSelfAttentionBlock(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+
+ sequence_state_dim = config.sequence_state_dim
+ pairwise_state_dim = config.pairwise_state_dim
+ sequence_num_heads = sequence_state_dim // config.sequence_head_width
+ pairwise_num_heads = pairwise_state_dim // config.pairwise_head_width
+
+ self.layernorm_1 = nn.LayerNorm(sequence_state_dim)
+
+ self.sequence_to_pair = EsmFoldSequenceToPair(sequence_state_dim, pairwise_state_dim // 2, pairwise_state_dim)
+ self.pair_to_sequence = EsmFoldPairToSequence(pairwise_state_dim, sequence_num_heads)
+
+ self.seq_attention = EsmFoldSelfAttention(
+ sequence_state_dim, sequence_num_heads, config.sequence_head_width, gated=True
+ )
+ self.tri_mul_out = EsmFoldTriangleMultiplicativeUpdate(config, _outgoing=True)
+ self.tri_mul_in = EsmFoldTriangleMultiplicativeUpdate(config, _outgoing=False)
+
+ self.tri_att_start = EsmFoldTriangleAttention(
+ pairwise_state_dim, config.pairwise_head_width, pairwise_num_heads, inf=1e9, starting=True
+ )
+ self.tri_att_end = EsmFoldTriangleAttention(
+ pairwise_state_dim, config.pairwise_head_width, pairwise_num_heads, inf=1e9, starting=False
+ )
+
+ self.mlp_seq = EsmFoldResidueMLP(sequence_state_dim, 4 * sequence_state_dim, dropout=config.dropout)
+ self.mlp_pair = EsmFoldResidueMLP(pairwise_state_dim, 4 * pairwise_state_dim, dropout=config.dropout)
+
+ self.drop = nn.Dropout(config.dropout)
+ self.row_drop = EsmFoldDropout(config.dropout * 2, 2)
+ self.col_drop = EsmFoldDropout(config.dropout * 2, 1)
+
+ def forward(self, sequence_state, pairwise_state, mask=None, chunk_size=None, **__kwargs):
+ """
+ Inputs:
+ sequence_state: B x L x sequence_state_dim pairwise_state: B x L x L x pairwise_state_dim mask: B x L boolean
+ tensor of valid positions
+
+ Output:
+ sequence_state: B x L x sequence_state_dim pairwise_state: B x L x L x pairwise_state_dim
+ """
+ if len(sequence_state.shape) != 3:
+ raise ValueError(f"`sequence_state` should be a 3d-tensor, got {len(sequence_state.shape)} dims.")
+ if len(pairwise_state.shape) != 4:
+ raise ValueError(f"`pairwise_state` should be a 4d-tensor, got {len(pairwise_state.shape)} dims.")
+ if mask is not None and len(mask.shape) != 2:
+ raise ValueError(f"`mask` should be a 2d-tensor, got {len(mask.shape)} dims.")
+
+ batch_dim, seq_dim, sequence_state_dim = sequence_state.shape
+ pairwise_state_dim = pairwise_state.shape[3]
+
+ if sequence_state_dim != self.config.sequence_state_dim:
+ raise ValueError(
+ "`sequence_state` last dimension should be equal to `self.sequence_state_dim`. Got"
+ f"{sequence_state_dim} != {self.config.sequence_state_dim}."
+ )
+ if pairwise_state_dim != self.config.pairwise_state_dim:
+ raise ValueError(
+ "`pairwise_state` last dimension should be equal to `self.pairwise_state_dim`. Got "
+ f"{pairwise_state_dim} != {self.config.pairwise_state_dim}."
+ )
+ if batch_dim != pairwise_state.shape[0]:
+ raise ValueError(
+ f"`sequence_state` and `pairwise_state` have inconsistent batch size: {batch_dim} != "
+ f"{pairwise_state.shape[0]}."
+ )
+ if seq_dim != pairwise_state.shape[1] or seq_dim != pairwise_state.shape[2]:
+ raise ValueError(
+ f"`sequence_state` and `pairwise_state` have inconsistent sequence length: {seq_dim} != "
+ f"{pairwise_state.shape[1]} or {pairwise_state.shape[2]}."
+ )
+
+ # Update sequence state
+ bias = self.pair_to_sequence(pairwise_state)
+
+ # Self attention with bias + mlp.
+ y = self.layernorm_1(sequence_state)
+ y, _ = self.seq_attention(y, mask=mask, bias=bias)
+ sequence_state = sequence_state + self.drop(y)
+ sequence_state = self.mlp_seq(sequence_state)
+
+ # Update pairwise state
+ pairwise_state = pairwise_state + self.sequence_to_pair(sequence_state)
+
+ # Axial attention with triangular bias.
+ tri_mask = mask.unsqueeze(2) * mask.unsqueeze(1) if mask is not None else None
+ pairwise_state = pairwise_state + self.row_drop(self.tri_mul_out(pairwise_state, mask=tri_mask))
+ pairwise_state = pairwise_state + self.col_drop(self.tri_mul_in(pairwise_state, mask=tri_mask))
+ pairwise_state = pairwise_state + self.row_drop(
+ self.tri_att_start(pairwise_state, mask=tri_mask, chunk_size=chunk_size)
+ )
+ pairwise_state = pairwise_state + self.col_drop(
+ self.tri_att_end(pairwise_state, mask=tri_mask, chunk_size=chunk_size)
+ )
+
+ # MLP over pairs.
+ pairwise_state = self.mlp_pair(pairwise_state)
+
+ return sequence_state, pairwise_state
+
+
+class EsmCategoricalMixture:
+ def __init__(self, param, bins=50, start=0, end=1):
+ # All tensors are of shape ..., bins.
+ self.logits = param
+ bins = torch.linspace(start, end, bins + 1, device=self.logits.device, dtype=self.logits.dtype)
+ self.v_bins = (bins[:-1] + bins[1:]) / 2
+
+ def log_prob(self, true):
+ # Shapes are:
+ # self.probs: ... x bins
+ # true : ...
+ true_index = (true.unsqueeze(-1) - self.v_bins[[None] * true.ndim]).abs().argmin(-1)
+ nll = self.logits.log_softmax(-1)
+ return torch.take_along_dim(nll, true_index.unsqueeze(-1), dim=-1).squeeze(-1)
+
+ def mean(self):
+ return (self.logits.softmax(-1) @ self.v_bins.unsqueeze(1)).squeeze(-1)
+
+
+def categorical_lddt(logits, bins=50):
+ # Logits are ..., 37, bins.
+ return EsmCategoricalMixture(logits, bins=bins).mean()
+
+
+def get_axial_mask(mask):
+ """
+ Helper to convert B x L mask of valid positions to axial mask used in row column attentions.
+
+ Input:
+ mask: B x L tensor of booleans
+
+ Output:
+ mask: B x L x L tensor of booleans
+ """
+
+ if mask is None:
+ return None
+
+ if len(mask.shape) != 2:
+ raise ValueError(f"`mask` should be a 2d-tensor, got {len(mask.shape)} dims.")
+ batch_dim, seq_dim = mask.shape
+ m = mask.unsqueeze(1).expand(batch_dim, seq_dim, seq_dim)
+ m = m.reshape(batch_dim * seq_dim, seq_dim)
+ return m
+
+
+class EsmFoldRelativePosition(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.bins = config.position_bins
+
+ # Note an additional offset is used so that the 0th position
+ # is reserved for masked pairs.
+ self.embedding = torch.nn.Embedding(2 * self.bins + 2, config.pairwise_state_dim)
+
+ def forward(self, residue_index, mask=None):
+ """
+ Input:
+ residue_index: B x L tensor of indices (dytpe=torch.long) mask: B x L tensor of booleans
+
+ Output:
+ pairwise_state: B x L x L x pairwise_state_dim tensor of embeddings
+ """
+ if residue_index.dtype != torch.long:
+ raise ValueError(f"`residue_index` has dtype {residue_index.dtype}, it should be `torch.long`.")
+ if mask is not None and residue_index.shape != mask.shape:
+ raise ValueError(
+ f"`residue_index` and `mask` have inconsistent shapes: {residue_index.shape} != {mask.shape}."
+ )
+
+ diff = residue_index[:, None, :] - residue_index[:, :, None]
+ diff = diff.clamp(-self.bins, self.bins)
+ diff = diff + self.bins + 1 # Add 1 to adjust for padding index.
+
+ if mask is not None:
+ mask = mask[:, None, :] * mask[:, :, None]
+ diff[mask == False] = 0 # noqa: E712
+
+ output = self.embedding(diff)
+ return output
+
+
+class EsmFoldAngleResnetBlock(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.linear_1 = EsmFoldLinear(config.resnet_dim, config.resnet_dim, init="relu")
+ self.linear_2 = EsmFoldLinear(config.resnet_dim, config.resnet_dim, init="final")
+
+ self.relu = nn.ReLU()
+
+ def forward(self, a: torch.Tensor) -> torch.Tensor:
+ s_initial = a
+
+ a = self.relu(a)
+ a = self.linear_1(a)
+ a = self.relu(a)
+ a = self.linear_2(a)
+
+ return a + s_initial
+
+
+class EsmFoldAngleResnet(nn.Module):
+ """
+ Implements Algorithm 20, lines 11-14
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+
+ self.linear_in = EsmFoldLinear(config.sequence_dim, config.resnet_dim)
+ self.linear_initial = EsmFoldLinear(config.sequence_dim, config.resnet_dim)
+
+ self.layers = nn.ModuleList()
+ for _ in range(config.num_resnet_blocks):
+ layer = EsmFoldAngleResnetBlock(config)
+ self.layers.append(layer)
+
+ self.linear_out = EsmFoldLinear(config.resnet_dim, config.num_angles * 2)
+
+ self.relu = nn.ReLU()
+
+ def forward(self, s: torch.Tensor, s_initial: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Args:
+ s:
+ [*, C_hidden] single embedding
+ s_initial:
+ [*, C_hidden] single embedding as of the start of the StructureModule
+ Returns:
+ [*, no_angles, 2] predicted angles
+ """
+ # NOTE: The ReLU's applied to the inputs are absent from the supplement
+ # pseudocode but present in the source. For maximal compatibility with
+ # the pretrained weights, I'm going with the source.
+
+ # [*, C_hidden]
+ s_initial = self.relu(s_initial)
+ s_initial = self.linear_initial(s_initial)
+ s = self.relu(s)
+ s = self.linear_in(s)
+ s = s + s_initial
+
+ for l in self.layers:
+ s = l(s)
+
+ s = self.relu(s)
+
+ # [*, no_angles * 2]
+ s = self.linear_out(s)
+
+ # [*, no_angles, 2]
+ s = s.view(s.shape[:-1] + (-1, 2))
+
+ unnormalized_s = s
+ norm_denom = torch.sqrt(
+ torch.clamp(
+ torch.sum(s**2, dim=-1, keepdim=True),
+ min=self.config.epsilon,
+ )
+ )
+ s = s / norm_denom
+
+ return unnormalized_s, s
+
+
+class EsmFoldInvariantPointAttention(nn.Module):
+ """
+ Implements Algorithm 22.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+
+ c_s = config.sequence_dim
+ c_z = config.pairwise_dim
+ self.hidden_dim = config.ipa_dim
+ self.num_heads = config.num_heads_ipa
+ self.num_qk_points = config.num_qk_points
+ self.num_v_points = config.num_v_points
+
+ # These linear layers differ from their specifications in the
+ # supplement. There, they lack bias and use Glorot initialization.
+ # Here as in the official source, they have bias and use the default
+ # Lecun initialization.
+ hc = config.ipa_dim * config.num_heads_ipa
+ self.linear_q = EsmFoldLinear(c_s, hc)
+ self.linear_kv = EsmFoldLinear(c_s, 2 * hc)
+
+ hpq = config.num_heads_ipa * config.num_qk_points * 3
+ self.linear_q_points = EsmFoldLinear(c_s, hpq)
+
+ hpkv = config.num_heads_ipa * (config.num_qk_points + config.num_v_points) * 3
+ self.linear_kv_points = EsmFoldLinear(c_s, hpkv)
+
+ self.linear_b = EsmFoldLinear(c_z, config.num_heads_ipa)
+
+ self.head_weights = nn.Parameter(torch.zeros((config.num_heads_ipa)))
+
+ concat_out_dim = config.num_heads_ipa * (c_z + config.ipa_dim + config.num_v_points * 4)
+ self.linear_out = EsmFoldLinear(concat_out_dim, c_s, init="final")
+
+ self.softmax = nn.Softmax(dim=-1)
+ self.softplus = nn.Softplus()
+
+ def forward(
+ self,
+ s: torch.Tensor,
+ z: Optional[torch.Tensor],
+ r: Rigid,
+ mask: torch.Tensor,
+ _offload_inference: bool = False,
+ _z_reference_list: Optional[Sequence[torch.Tensor]] = None,
+ ) -> torch.Tensor:
+ """
+ Args:
+ s:
+ [*, N_res, C_s] single representation
+ z:
+ [*, N_res, N_res, C_z] pair representation
+ r:
+ [*, N_res] transformation object
+ mask:
+ [*, N_res] mask
+ Returns:
+ [*, N_res, C_s] single representation update
+ """
+ z = [z]
+
+ #######################################
+ # Generate scalar and point activations
+ #######################################
+ # [*, N_res, H * C_hidden]
+ q = self.linear_q(s)
+ kv = self.linear_kv(s)
+
+ # [*, N_res, H, C_hidden]
+ q = q.view(q.shape[:-1] + (self.num_heads, -1))
+
+ # [*, N_res, H, 2 * C_hidden]
+ kv = kv.view(kv.shape[:-1] + (self.num_heads, -1))
+
+ # [*, N_res, H, C_hidden]
+ k, v = torch.split(kv, self.hidden_dim, dim=-1)
+
+ # [*, N_res, H * P_q * 3]
+ q_pts = self.linear_q_points(s)
+
+ # This is kind of clunky, but it's how the original does it
+ # [*, N_res, H * P_q, 3]
+ q_pts = torch.split(q_pts, q_pts.shape[-1] // 3, dim=-1)
+ q_pts = torch.stack(q_pts, dim=-1)
+ q_pts = r[..., None].apply(q_pts)
+
+ # [*, N_res, H, P_q, 3]
+ q_pts = q_pts.view(q_pts.shape[:-2] + (self.num_heads, self.num_qk_points, 3))
+
+ # [*, N_res, H * (P_q + P_v) * 3]
+ kv_pts = self.linear_kv_points(s)
+
+ # [*, N_res, H * (P_q + P_v), 3]
+ kv_pts = torch.split(kv_pts, kv_pts.shape[-1] // 3, dim=-1)
+ kv_pts = torch.stack(kv_pts, dim=-1)
+ kv_pts = r[..., None].apply(kv_pts)
+
+ # [*, N_res, H, (P_q + P_v), 3]
+ kv_pts = kv_pts.view(kv_pts.shape[:-2] + (self.num_heads, -1, 3))
+
+ # [*, N_res, H, P_q/P_v, 3]
+ k_pts, v_pts = torch.split(kv_pts, [self.num_qk_points, self.num_v_points], dim=-2)
+
+ ##########################
+ # Compute attention scores
+ ##########################
+ # [*, N_res, N_res, H]
+ b = self.linear_b(z[0])
+
+ if _offload_inference:
+ assert sys.getrefcount(z[0]) == 2
+ z[0] = z[0].cpu()
+
+ # [*, H, N_res, N_res]
+ if is_fp16_enabled():
+ with torch.cuda.amp.autocast(enabled=False):
+ a = torch.matmul(
+ permute_final_dims(q.float(), (1, 0, 2)), # [*, H, N_res, C_hidden]
+ permute_final_dims(k.float(), (1, 2, 0)), # [*, H, C_hidden, N_res]
+ )
+ else:
+ a = torch.matmul(
+ permute_final_dims(q, (1, 0, 2)), # [*, H, N_res, C_hidden]
+ permute_final_dims(k, (1, 2, 0)), # [*, H, C_hidden, N_res]
+ )
+
+ a *= math.sqrt(1.0 / (3 * self.hidden_dim))
+ a += math.sqrt(1.0 / 3) * permute_final_dims(b, (2, 0, 1))
+
+ # [*, N_res, N_res, H, P_q, 3]
+ pt_att = q_pts.unsqueeze(-4) - k_pts.unsqueeze(-5)
+ pt_att = pt_att**2
+
+ # [*, N_res, N_res, H, P_q]
+ pt_att = sum(torch.unbind(pt_att, dim=-1))
+ head_weights = self.softplus(self.head_weights).view(*((1,) * len(pt_att.shape[:-2]) + (-1, 1)))
+ head_weights = head_weights * math.sqrt(1.0 / (3 * (self.num_qk_points * 9.0 / 2)))
+ pt_att = pt_att * head_weights
+
+ # [*, N_res, N_res, H]
+ pt_att = torch.sum(pt_att, dim=-1) * (-0.5)
+ # [*, N_res, N_res]
+ square_mask = mask.unsqueeze(-1) * mask.unsqueeze(-2)
+ square_mask = self.config.inf * (square_mask - 1)
+
+ # [*, H, N_res, N_res]
+ pt_att = permute_final_dims(pt_att, (2, 0, 1))
+
+ a = a + pt_att
+ a = a + square_mask.unsqueeze(-3)
+ a = self.softmax(a)
+
+ ################
+ # Compute output
+ ################
+ # [*, N_res, H, C_hidden]
+ o = torch.matmul(a, v.transpose(-2, -3).to(dtype=a.dtype)).transpose(-2, -3)
+
+ # [*, N_res, H * C_hidden]
+ o = flatten_final_dims(o, 2)
+
+ # [*, H, 3, N_res, P_v]
+ o_pt = torch.sum(
+ (a[..., None, :, :, None] * permute_final_dims(v_pts, (1, 3, 0, 2))[..., None, :, :]),
+ dim=-2,
+ )
+
+ # [*, N_res, H, P_v, 3]
+ o_pt = permute_final_dims(o_pt, (2, 0, 3, 1))
+ o_pt = r[..., None, None].invert_apply(o_pt)
+
+ # [*, N_res, H * P_v]
+ o_pt_norm = flatten_final_dims(torch.sqrt(torch.sum(o_pt**2, dim=-1) + self.config.epsilon), 2)
+
+ # [*, N_res, H * P_v, 3]
+ o_pt = o_pt.reshape(*o_pt.shape[:-3], -1, 3)
+
+ if _offload_inference:
+ z[0] = z[0].to(o_pt.device)
+
+ # [*, N_res, H, C_z]
+ o_pair = torch.matmul(a.transpose(-2, -3), z[0].to(dtype=a.dtype))
+
+ # [*, N_res, H * C_z]
+ o_pair = flatten_final_dims(o_pair, 2)
+
+ # [*, N_res, C_s]
+ s = self.linear_out(
+ torch.cat((o, *torch.unbind(o_pt, dim=-1), o_pt_norm, o_pair), dim=-1).to(dtype=z[0].dtype)
+ )
+
+ return s
+
+
+class EsmFoldBackboneUpdate(nn.Module):
+ """
+ Implements part of Algorithm 23.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+
+ self.linear = EsmFoldLinear(config.sequence_dim, 6, init="final")
+
+ def forward(self, s: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Args:
+ [*, N_res, C_s] single representation
+ Returns:
+ [*, N_res, 6] update vector
+ """
+ # [*, 6]
+ update = self.linear(s)
+
+ return update
+
+
+class EsmFoldStructureModuleTransitionLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.linear_1 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="relu")
+ self.linear_2 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="relu")
+ self.linear_3 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="final")
+
+ self.relu = nn.ReLU()
+
+ def forward(self, s):
+ s_initial = s
+ s = self.linear_1(s)
+ s = self.relu(s)
+ s = self.linear_2(s)
+ s = self.relu(s)
+ s = self.linear_3(s)
+
+ s = s + s_initial
+
+ return s
+
+
+class EsmFoldStructureModuleTransition(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+
+ self.layers = nn.ModuleList()
+ for _ in range(config.num_transition_layers):
+ l = EsmFoldStructureModuleTransitionLayer(config)
+ self.layers.append(l)
+
+ self.dropout = nn.Dropout(config.dropout_rate)
+ self.layer_norm = LayerNorm(config.sequence_dim)
+
+ def forward(self, s):
+ for l in self.layers:
+ s = l(s)
+
+ s = self.dropout(s)
+ s = self.layer_norm(s)
+
+ return s
+
+
+class EsmFoldStructureModule(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+
+ # Buffers to be lazily initialized later
+ # self.default_frames
+ # self.group_idx
+ # self.atom_mask
+ # self.lit_positions
+
+ self.layer_norm_s = LayerNorm(config.sequence_dim)
+ self.layer_norm_z = LayerNorm(config.pairwise_dim)
+
+ self.linear_in = EsmFoldLinear(config.sequence_dim, config.sequence_dim)
+
+ self.ipa = EsmFoldInvariantPointAttention(config)
+
+ self.ipa_dropout = nn.Dropout(config.dropout_rate)
+ self.layer_norm_ipa = LayerNorm(config.sequence_dim)
+
+ self.transition = EsmFoldStructureModuleTransition(config)
+ self.bb_update = EsmFoldBackboneUpdate(config)
+ self.angle_resnet = EsmFoldAngleResnet(config)
+
+ def forward(
+ self,
+ evoformer_output_dict,
+ aatype,
+ mask=None,
+ _offload_inference=False,
+ ):
+ """
+ Args:
+ evoformer_output_dict:
+ Dictionary containing:
+ "single":
+ [*, N_res, C_s] single representation
+ "pair":
+ [*, N_res, N_res, C_z] pair representation
+ aatype:
+ [*, N_res] amino acid indices
+ mask:
+ Optional [*, N_res] sequence mask
+ Returns:
+ A dictionary of outputs
+ """
+ s = evoformer_output_dict["single"]
+
+ if mask is None:
+ # [*, N]
+ mask = s.new_ones(s.shape[:-1])
+
+ # [*, N, C_s]
+ s = self.layer_norm_s(s)
+
+ # [*, N, N, C_z]
+ z = self.layer_norm_z(evoformer_output_dict["pair"])
+
+ z_reference_list = None
+ if _offload_inference:
+ assert sys.getrefcount(evoformer_output_dict["pair"]) == 2
+ evoformer_output_dict["pair"] = evoformer_output_dict["pair"].cpu()
+ z_reference_list = [z]
+ z = None
+
+ # [*, N, C_s]
+ s_initial = s
+ s = self.linear_in(s)
+
+ # [*, N]
+ rigids = Rigid.identity(
+ s.shape[:-1],
+ s.dtype,
+ s.device,
+ self.training,
+ fmt="quat",
+ )
+ outputs = []
+ for i in range(self.config.num_blocks):
+ # [*, N, C_s]
+ s = s + self.ipa(
+ s,
+ z,
+ rigids,
+ mask,
+ _offload_inference=_offload_inference,
+ _z_reference_list=z_reference_list,
+ )
+ s = self.ipa_dropout(s)
+ s = self.layer_norm_ipa(s)
+ s = self.transition(s)
+
+ # [*, N]
+ rigids = rigids.compose_q_update_vec(self.bb_update(s))
+
+ # To hew as closely as possible to AlphaFold, we convert our
+ # quaternion-based transformations to rotation-matrix ones
+ # here
+ backb_to_global = Rigid(
+ Rotation(rot_mats=rigids.get_rots().get_rot_mats(), quats=None),
+ rigids.get_trans(),
+ )
+
+ backb_to_global = backb_to_global.scale_translation(self.config.trans_scale_factor)
+
+ # [*, N, 7, 2]
+ unnormalized_angles, angles = self.angle_resnet(s, s_initial)
+
+ all_frames_to_global = self.torsion_angles_to_frames(backb_to_global, angles, aatype)
+
+ pred_xyz = self.frames_and_literature_positions_to_atom14_pos(all_frames_to_global, aatype)
+
+ scaled_rigids = rigids.scale_translation(self.config.trans_scale_factor)
+
+ preds = {
+ "frames": scaled_rigids.to_tensor_7(),
+ "sidechain_frames": all_frames_to_global.to_tensor_4x4(),
+ "unnormalized_angles": unnormalized_angles,
+ "angles": angles,
+ "positions": pred_xyz,
+ "states": s,
+ }
+
+ outputs.append(preds)
+
+ rigids = rigids.stop_rot_gradient()
+
+ del z, z_reference_list
+
+ if _offload_inference:
+ evoformer_output_dict["pair"] = evoformer_output_dict["pair"].to(s.device)
+
+ outputs = dict_multimap(torch.stack, outputs)
+ outputs["single"] = s
+
+ return outputs
+
+ def _init_residue_constants(self, float_dtype, device):
+ if not hasattr(self, "default_frames"):
+ self.register_buffer(
+ "default_frames",
+ torch.tensor(
+ residue_constants.restype_rigid_group_default_frame,
+ dtype=float_dtype,
+ device=device,
+ requires_grad=False,
+ ),
+ persistent=False,
+ )
+ if not hasattr(self, "group_idx"):
+ self.register_buffer(
+ "group_idx",
+ torch.tensor(
+ residue_constants.restype_atom14_to_rigid_group,
+ device=device,
+ requires_grad=False,
+ ),
+ persistent=False,
+ )
+ if not hasattr(self, "atom_mask"):
+ self.register_buffer(
+ "atom_mask",
+ torch.tensor(
+ residue_constants.restype_atom14_mask,
+ dtype=float_dtype,
+ device=device,
+ requires_grad=False,
+ ),
+ persistent=False,
+ )
+ if not hasattr(self, "lit_positions"):
+ self.register_buffer(
+ "lit_positions",
+ torch.tensor(
+ residue_constants.restype_atom14_rigid_group_positions,
+ dtype=float_dtype,
+ device=device,
+ requires_grad=False,
+ ),
+ persistent=False,
+ )
+
+ def torsion_angles_to_frames(self, r, alpha, f):
+ # Lazily initialize the residue constants on the correct device
+ self._init_residue_constants(alpha.dtype, alpha.device)
+ # Separated purely to make testing less annoying
+ return torsion_angles_to_frames(r, alpha, f, self.default_frames)
+
+ def frames_and_literature_positions_to_atom14_pos(self, r, f): # [*, N, 8] # [*, N]
+ # Lazily initialize the residue constants on the correct device
+ self._init_residue_constants(r.get_rots().dtype, r.get_rots().device)
+ return frames_and_literature_positions_to_atom14_pos(
+ r,
+ f,
+ self.default_frames,
+ self.group_idx,
+ self.atom_mask,
+ self.lit_positions,
+ )
+
+
+class EsmFoldingTrunk(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+
+ c_s = config.sequence_state_dim
+ c_z = config.pairwise_state_dim
+
+ self.pairwise_positional_embedding = EsmFoldRelativePosition(config)
+
+ self.blocks = nn.ModuleList([EsmFoldTriangularSelfAttentionBlock(config) for _ in range(config.num_blocks)])
+
+ self.recycle_bins = 15
+ self.recycle_s_norm = nn.LayerNorm(c_s)
+ self.recycle_z_norm = nn.LayerNorm(c_z)
+ self.recycle_disto = nn.Embedding(self.recycle_bins, c_z)
+ self.recycle_disto.weight[0].detach().zero_()
+
+ self.structure_module = EsmFoldStructureModule(config.structure_module)
+ self.trunk2sm_s = nn.Linear(c_s, config.structure_module.sequence_dim)
+ self.trunk2sm_z = nn.Linear(c_z, config.structure_module.pairwise_dim)
+
+ self.chunk_size = config.chunk_size
+
+ def set_chunk_size(self, chunk_size):
+ # This parameter means the axial attention will be computed
+ # in a chunked manner. This should make the memory used more or less O(L) instead of O(L^2).
+ # It's equivalent to running a for loop over chunks of the dimension we're iterative over,
+ # where the chunk_size is the size of the chunks, so 128 would mean to parse 128-lengthed chunks.
+ self.chunk_size = chunk_size
+
+ def forward(self, seq_feats, pair_feats, true_aa, residx, mask, no_recycles):
+ """
+ Inputs:
+ seq_feats: B x L x C tensor of sequence features pair_feats: B x L x L x C tensor of pair features residx: B
+ x L long tensor giving the position in the sequence mask: B x L boolean tensor indicating valid residues
+
+ Output:
+ predicted_structure: B x L x (num_atoms_per_residue * 3) tensor wrapped in a Coordinates object
+ """
+
+ device = seq_feats.device
+ s_s_0 = seq_feats
+ s_z_0 = pair_feats
+
+ if no_recycles is None:
+ no_recycles = self.config.max_recycles
+ else:
+ if no_recycles < 0:
+ raise ValueError("Number of recycles must not be negative.")
+ no_recycles += 1 # First 'recycle' is just the standard forward pass through the model.
+
+ def trunk_iter(s, z, residx, mask):
+ z = z + self.pairwise_positional_embedding(residx, mask=mask)
+
+ for block in self.blocks:
+ s, z = block(s, z, mask=mask, residue_index=residx, chunk_size=self.chunk_size)
+ return s, z
+
+ s_s = s_s_0
+ s_z = s_z_0
+ recycle_s = torch.zeros_like(s_s)
+ recycle_z = torch.zeros_like(s_z)
+ recycle_bins = torch.zeros(*s_z.shape[:-1], device=device, dtype=torch.int64)
+
+ for recycle_idx in range(no_recycles):
+ with ContextManagers([] if recycle_idx == no_recycles - 1 else [torch.no_grad()]):
+ # === Recycling ===
+ recycle_s = self.recycle_s_norm(recycle_s.detach()).to(device)
+ recycle_z = self.recycle_z_norm(recycle_z.detach()).to(device)
+ recycle_z += self.recycle_disto(recycle_bins.detach()).to(device)
+
+ s_s, s_z = trunk_iter(s_s_0 + recycle_s, s_z_0 + recycle_z, residx, mask)
+
+ # === Structure module ===
+ structure = self.structure_module(
+ {"single": self.trunk2sm_s(s_s), "pair": self.trunk2sm_z(s_z)},
+ true_aa,
+ mask.float(),
+ )
+
+ recycle_s = s_s
+ recycle_z = s_z
+ # Distogram needs the N, CA, C coordinates, and bin constants same as alphafold.
+ recycle_bins = EsmFoldingTrunk.distogram(
+ structure["positions"][-1][:, :, :3],
+ 3.375,
+ 21.375,
+ self.recycle_bins,
+ )
+
+ structure["s_s"] = s_s
+ structure["s_z"] = s_z
+
+ return structure
+
+ @staticmethod
+ def distogram(coords, min_bin, max_bin, num_bins):
+ # Coords are [... L x 3 x 3], where it's [N, CA, C] x 3 coordinates.
+ boundaries = torch.linspace(
+ min_bin,
+ max_bin,
+ num_bins - 1,
+ device=coords.device,
+ )
+ boundaries = boundaries**2
+ N, CA, C = [x.squeeze(-2) for x in coords.chunk(3, dim=-2)]
+ # Infer CB coordinates.
+ b = CA - N
+ c = C - CA
+ a = b.cross(c, dim=-1)
+ CB = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + CA
+ dists = (CB[..., None, :, :] - CB[..., :, None, :]).pow(2).sum(dim=-1, keepdims=True)
+ bins = torch.sum(dists > boundaries, dim=-1) # [..., L, L]
+ return bins
+
+
+# TODO Add information to the docstring about any methods that convert to PDB format, or otherwise prepare
+# the outputs for downstream use.
+
+
+@add_start_docstrings(
+ """
+ ESMForProteinFolding is the HuggingFace port of the original ESMFold model. It consists of an ESM-2 "stem" followed
+ by a protein folding "head", although unlike most other output heads, this "head" is similar in size and runtime to
+ the rest of the model combined! It outputs a dictionary containing predicted structural information about the input
+ protein(s).
+ """,
+ ESM_START_DOCSTRING,
+)
+class EsmForProteinFolding(EsmPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.config = config
+
+ self.distogram_bins = 64
+
+ self.esm = EsmModel(config, add_pooling_layer=False)
+
+ self.esm.requires_grad_(False)
+ if self.config.esmfold_config.fp16_esm:
+ self.esm.half()
+
+ self.esm_feats = self.config.hidden_size
+ self.esm_attns = self.config.num_hidden_layers * self.config.num_attention_heads
+ self.esm_layers = self.config.num_hidden_layers
+ self.register_buffer("af2_to_esm", self._af2_to_esm_from_vocab_list(config.vocab_list))
+ self.esm_s_combine = nn.Parameter(torch.zeros(self.esm_layers + 1))
+
+ trunk_config = self.config.esmfold_config.trunk
+ c_s = trunk_config.sequence_state_dim
+ c_z = trunk_config.pairwise_state_dim
+ self.esm_s_mlp = nn.Sequential(
+ LayerNorm(self.esm_feats),
+ nn.Linear(self.esm_feats, c_s),
+ nn.ReLU(),
+ nn.Linear(c_s, c_s),
+ )
+
+ # 0 is padding, N is unknown residues, N + 1 is mask.
+ self.n_tokens_embed = residue_constants.restype_num + 3
+ self.pad_idx = 0
+ self.unk_idx = self.n_tokens_embed - 2
+ self.mask_idx = self.n_tokens_embed - 1
+ self.esm_dict_cls_idx = self.config.vocab_list.index("")
+ self.esm_dict_mask_idx = self.config.vocab_list.index("")
+ self.esm_dict_eos_idx = self.config.vocab_list.index("")
+ self.esm_dict_padding_idx = self.config.vocab_list.index("")
+ if self.config.esmfold_config.embed_aa:
+ self.embedding = nn.Embedding(self.n_tokens_embed, c_s, padding_idx=0)
+
+ self.trunk = EsmFoldingTrunk(trunk_config)
+
+ self.distogram_head = nn.Linear(c_z, self.distogram_bins)
+ self.ptm_head = nn.Linear(c_z, self.distogram_bins)
+ self.lm_head = nn.Linear(c_s, self.n_tokens_embed)
+ self.lddt_bins = 50
+ structure_module_config = trunk_config.structure_module
+ self.lddt_head = nn.Sequential(
+ nn.LayerNorm(structure_module_config.sequence_dim),
+ nn.Linear(structure_module_config.sequence_dim, self.config.esmfold_config.lddt_head_hid_dim),
+ nn.Linear(self.config.esmfold_config.lddt_head_hid_dim, self.config.esmfold_config.lddt_head_hid_dim),
+ nn.Linear(self.config.esmfold_config.lddt_head_hid_dim, 37 * self.lddt_bins),
+ )
+
+ @staticmethod
+ def _af2_to_esm_from_vocab_list(vocab_list: List[str]) -> torch.Tensor:
+ # Remember that t is shifted from residue_constants by 1 (0 is padding).
+ esm_reorder = [vocab_list.index("")] + [vocab_list.index(v) for v in residue_constants.restypes_with_x]
+ return torch.tensor(esm_reorder)
+
+ @add_start_docstrings_to_model_forward(ESMFOLD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(output_type=EsmForProteinFoldingOutput, config_class=EsmConfig)
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ attention_mask: torch.Tensor = None,
+ position_ids: Optional[torch.Tensor] = None,
+ masking_pattern: Optional[torch.Tensor] = None,
+ num_recycles: Optional[int] = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, EsmForProteinFolding
+
+ >>> model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1")
+ >>> inputs = tokenizer(["MLKNVQVQLV"], return_tensors="pt", add_special_tokens=False) # A tiny random peptide
+ >>> outputs = model(**inputs)
+ >>> folded_positions = outputs.positions
+ ```
+
+ """
+ cfg = self.config.esmfold_config
+
+ aa = input_ids # B x L
+ B = aa.shape[0]
+ L = aa.shape[1]
+ device = input_ids.device
+ if attention_mask is None:
+ attention_mask = torch.ones_like(aa, device=device)
+ if position_ids is None:
+ position_ids = torch.arange(L, device=device).expand_as(input_ids)
+
+ # === ESM ===
+ esmaa = self.af2_idx_to_esm_idx(aa, attention_mask)
+
+ if masking_pattern is not None:
+ masked_aa, esmaa, mlm_targets = self.bert_mask(aa, esmaa, attention_mask, masking_pattern)
+ else:
+ masked_aa = aa
+ mlm_targets = None
+
+ # We get sequence and pair representations from whatever version of ESM /
+ # configuration we are using. The sequence representation esm_s is always
+ # present. The pair embedding esm_z may be present depending on the
+ # configuration of the model. If esm_z is not used by the model then it
+ # is returned as None here.
+ esm_s = self.compute_language_model_representations(esmaa)
+
+ # Convert esm_s and esm_z, if present, to the precision used by the trunk and
+ # the structure module. These tensors may be a lower precision if, for example,
+ # we're running the language model in fp16 precision.
+ esm_s = esm_s.to(self.esm_s_combine.dtype)
+
+ if cfg.esm_ablate_sequence:
+ esm_s = esm_s * 0
+
+ esm_s = esm_s.detach()
+
+ # === preprocessing ===
+ esm_s = (self.esm_s_combine.softmax(0).unsqueeze(0) @ esm_s).squeeze(2)
+ s_s_0 = self.esm_s_mlp(esm_s)
+
+ s_z_0 = s_s_0.new_zeros(B, L, L, cfg.trunk.pairwise_state_dim)
+
+ if self.config.esmfold_config.embed_aa:
+ s_s_0 += self.embedding(masked_aa)
+
+ structure: dict = self.trunk(s_s_0, s_z_0, aa, position_ids, attention_mask, no_recycles=num_recycles)
+ # Documenting what we expect:
+ structure = {
+ k: v
+ for k, v in structure.items()
+ if k
+ in [
+ "s_z",
+ "s_s",
+ "frames",
+ "sidechain_frames",
+ "unnormalized_angles",
+ "angles",
+ "positions",
+ "states",
+ ]
+ }
+
+ # Add BERT mask for the loss to use, if available.
+ if mlm_targets:
+ structure["mlm_targets"] = mlm_targets
+
+ disto_logits = self.distogram_head(structure["s_z"])
+ disto_logits = (disto_logits + disto_logits.transpose(1, 2)) / 2
+ structure["distogram_logits"] = disto_logits
+
+ lm_logits = self.lm_head(structure["s_s"])
+ structure["lm_logits"] = lm_logits
+
+ structure["aatype"] = aa
+ make_atom14_masks(structure)
+ # Of course, this doesn't respect the true mask because it doesn't know about it...
+ # We're not going to properly mask change of index tensors:
+ # "residx_atom14_to_atom37",
+ # "residx_atom37_to_atom14",
+ for k in [
+ "atom14_atom_exists",
+ "atom37_atom_exists",
+ ]:
+ structure[k] *= attention_mask.unsqueeze(-1)
+ structure["residue_index"] = position_ids
+
+ lddt_head = self.lddt_head(structure["states"]).reshape(structure["states"].shape[0], B, L, -1, self.lddt_bins)
+ structure["lddt_head"] = lddt_head
+ plddt = categorical_lddt(lddt_head[-1], bins=self.lddt_bins)
+ structure["plddt"] = plddt
+
+ ptm_logits = self.ptm_head(structure["s_z"])
+ structure["ptm_logits"] = ptm_logits
+ structure["ptm"] = compute_tm(ptm_logits, max_bin=31, no_bins=self.distogram_bins)
+ structure.update(compute_predicted_aligned_error(ptm_logits, max_bin=31, no_bins=self.distogram_bins))
+
+ return EsmForProteinFoldingOutput(**structure)
+
+ def af2_idx_to_esm_idx(self, aa, mask):
+ # avoid indexing on different devices
+ if self.af2_to_esm.device != aa.device:
+ self.af2_to_esm = self.af2_to_esm.to(aa.device)
+ aa = (aa + 1).masked_fill(mask != 1, 0)
+ return self.af2_to_esm[aa]
+
+ def compute_language_model_representations(self, esmaa: torch.Tensor) -> torch.Tensor:
+ device = next(self.parameters()).device
+ B, L = esmaa.shape # B = batch size, L = sequence length.
+
+ if self.config.esmfold_config.bypass_lm:
+ esm_s = torch.zeros(B, L, self.esm_s_combine.size[0], -1, self.esm_feats, device=device)
+ return esm_s
+
+ bosi, eosi = self.esm_dict_cls_idx, self.esm_dict_eos_idx
+ bos = esmaa.new_full((B, 1), bosi)
+ eos = esmaa.new_full((B, 1), self.esm_dict_padding_idx)
+ esmaa = torch.cat([bos, esmaa, eos], dim=1)
+ # Use the first padding index as eos during inference.
+ esmaa[range(B), (esmaa != 1).sum(1)] = eosi
+
+ # _, esm_z, esm_s = self.esm(esmaa, return_pairs=self.config.esmfold_config.use_esm_attn_map)
+ # Because we do not support use_esm_attn_map in the HF port as it is not used in any public models,
+ # esm_z is always None
+ esm_hidden_states = self.esm(esmaa, attention_mask=esmaa != 1, output_hidden_states=True)["hidden_states"]
+ esm_s = torch.stack(esm_hidden_states, dim=2)
+
+ esm_s = esm_s[:, 1:-1] # B, L, nLayers, C
+
+ return esm_s
+
+ def bert_mask(self, aa, esmaa, mask, pattern):
+ new_aa = aa.clone()
+ target = aa.clone()
+ new_esmaa = esmaa.clone()
+ new_aa[pattern == 1] = self.mask_idx
+ target[pattern != 1] = 0
+ new_esmaa[pattern == 1] = self.esm_dict_mask_idx
+ return new_aa, new_esmaa, target
+
+ @torch.no_grad()
+ def infer(
+ self,
+ seqs: Union[str, List[str]],
+ position_ids=None,
+ ):
+ if type(seqs) is str:
+ lst = [seqs]
+ else:
+ lst = seqs
+ # Returns the raw outputs of the model given an input sequence.
+ device = next(self.parameters()).device
+ aatype = collate_dense_tensors(
+ [
+ torch.from_numpy(
+ residue_constants.sequence_to_onehot(
+ sequence=seq,
+ mapping=residue_constants.restype_order_with_x,
+ map_unknown_to_x=True,
+ )
+ )
+ .to(device)
+ .argmax(dim=1)
+ for seq in lst
+ ]
+ ) # B=1 x L
+ mask = collate_dense_tensors([aatype.new_ones(len(seq)) for seq in lst])
+ position_ids = (
+ torch.arange(aatype.shape[1], device=device).expand(len(lst), -1)
+ if position_ids is None
+ else position_ids.to(device)
+ )
+ if position_ids.ndim == 1:
+ position_ids = position_ids.unsqueeze(0)
+ return self.forward(
+ aatype,
+ mask,
+ position_ids=position_ids,
+ )
+
+ @staticmethod
+ def output_to_pdb(output: Dict) -> List[str]:
+ """Returns the pbd (file) string from the model given the model output."""
+ output = {k: v.to("cpu").numpy() for k, v in output.items()}
+ pdbs = []
+ final_atom_positions = atom14_to_atom37(output["positions"][-1], output)
+ final_atom_mask = output["atom37_atom_exists"]
+ for i in range(output["aatype"].shape[0]):
+ aa = output["aatype"][i]
+ pred_pos = final_atom_positions[i]
+ mask = final_atom_mask[i]
+ resid = output["residue_index"][i] + 1
+ pred = OFProtein(
+ aatype=aa,
+ atom_positions=pred_pos,
+ atom_mask=mask,
+ residue_index=resid,
+ b_factors=output["plddt"][i],
+ )
+ pdbs.append(to_pdb(pred))
+ return pdbs
+
+ def infer_pdb(self, seqs, *args, **kwargs) -> str:
+ """Returns the pdb (file) string from the model given an input sequence."""
+ assert type(seqs) is str
+ output = self.infer(seqs, *args, **kwargs)
+ return self.output_to_pdb(output)[0]
+
+ def infer_pdbs(self, seqs: List[str], *args, **kwargs) -> List[str]:
+ """Returns the pdb (file) string from the model given an input sequence."""
+ output = self.infer(seqs, *args, **kwargs)
+ return self.output_to_pdb(output)
diff --git a/open_biomed/models/protein/mutaplm/modeling_esm.py b/open_biomed/models/protein/mutaplm/modeling_esm.py
new file mode 100644
index 0000000000000000000000000000000000000000..bac728da005453870e907ff0b6174077da1ab6e5
--- /dev/null
+++ b/open_biomed/models/protein/mutaplm/modeling_esm.py
@@ -0,0 +1,1390 @@
+# coding=utf-8
+# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch ESM model."""
+
+import copy
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+import torch.nn.functional as F
+
+from transformers.file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPastAndCrossAttentions,
+ BaseModelOutputWithPoolingAndCrossAttentions,
+ MaskedLMOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+ ModelOutput,
+)
+from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from transformers.utils import logging
+from transformers.models.esm.configuration_esm import EsmConfig
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
+_CONFIG_FOR_DOC = "EsmConfig"
+
+ESM_PRETRAINED_MODEL_ARCHIVE_LIST = [
+ "facebook/esm2_t6_8M_UR50D",
+ "facebook/esm2_t12_35M_UR50D",
+ # This is not a complete list of all ESM models!
+ # See all ESM models at https://huggingface.co/models?filter=esm
+]
+
+
+def rotate_half(x):
+ x1, x2 = x.chunk(2, dim=-1)
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(x, cos, sin):
+ cos = cos[:, :, : x.shape[-2], :]
+ sin = sin[:, :, : x.shape[-2], :]
+
+ return (x * cos) + (rotate_half(x) * sin)
+
+
+def gelu(x):
+ """
+ This is the gelu implementation from the original ESM repo. Using F.gelu yields subtly wrong results.
+ """
+ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+
+def symmetrize(x):
+ "Make layer symmetric in final two dimensions, used for contact prediction."
+ return x + x.transpose(-1, -2)
+
+
+def average_product_correct(x):
+ "Perform average product correct, used for contact prediction."
+ a1 = x.sum(-1, keepdims=True)
+ a2 = x.sum(-2, keepdims=True)
+ a12 = x.sum((-1, -2), keepdims=True)
+
+ avg = a1 * a2
+ avg.div_(a12) # in-place to reduce memory
+ normalized = x - avg
+ return normalized
+
+
+class RotaryEmbedding(torch.nn.Module):
+ """
+ Rotary position embeddings based on those in
+ [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
+ matrices which depend on their relative positions.
+ """
+
+ def __init__(self, dim: int):
+ super().__init__()
+ # Generate and save the inverse frequency buffer (non trainable)
+ inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+ inv_freq = inv_freq
+ self.register_buffer("inv_freq", inv_freq)
+
+ self._seq_len_cached = None
+ self._cos_cached = None
+ self._sin_cached = None
+
+ def _update_cos_sin_tables(self, x, seq_dimension=2):
+ seq_len = x.shape[seq_dimension]
+
+ # Reset the tables if the sequence length has changed,
+ # or if we're on a new device (possibly due to tracing for instance)
+ if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
+ self._seq_len_cached = seq_len
+ t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
+ freqs = torch.outer(t, self.inv_freq)
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+
+ self._cos_cached = emb.cos()[None, None, :, :]
+ self._sin_cached = emb.sin()[None, None, :, :]
+
+ return self._cos_cached, self._sin_cached
+
+ def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ self._cos_cached, self._sin_cached = self._update_cos_sin_tables(q, seq_dimension=-2)
+ rotated_q = apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached)
+
+ self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2)
+ rotated_k = apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached)
+
+ return (rotated_q, rotated_k)
+
+
+class EsmContactPredictionHead(nn.Module):
+ """Performs symmetrization, apc, and computes a logistic regression on the output features"""
+
+ def __init__(
+ self,
+ in_features: int,
+ bias=True,
+ eos_idx: int = 2,
+ ):
+ super().__init__()
+ self.in_features = in_features
+ self.eos_idx = eos_idx
+ self.regression = nn.Linear(in_features, 1, bias)
+ self.activation = nn.Sigmoid()
+
+ def forward(self, tokens, attentions):
+ # remove eos token attentions
+ eos_mask = tokens.ne(self.eos_idx).to(attentions)
+ eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
+ attentions = attentions * eos_mask[:, None, None, :, :]
+ attentions = attentions[..., :-1, :-1]
+ # remove cls token attentions
+ attentions = attentions[..., 1:, 1:]
+ batch_size, layers, heads, seqlen, _ = attentions.size()
+ attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
+
+ # features: batch x channels x tokens x tokens (symmetric)
+ attentions = attentions.to(
+ self.regression.weight.device
+ ) # attentions always float32, may need to convert to float16
+ attentions = average_product_correct(symmetrize(attentions))
+ attentions = attentions.permute(0, 2, 3, 1)
+ return self.activation(self.regression(attentions).squeeze(3))
+
+
+class EsmEmbeddings(nn.Module):
+ """
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+
+ if config.emb_layer_norm_before:
+ self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ else:
+ self.layer_norm = None
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+
+ self.padding_idx = config.pad_token_id
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+ )
+ self.token_dropout = config.token_dropout
+ self.mask_token_id = config.mask_token_id
+
+ def forward(
+ self, input_ids=None, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+ ):
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
+ else:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+
+ # Note that if we want to support ESM-1 (not 1b!) in future then we need to support an
+ # embedding_scale factor here.
+ embeddings = inputs_embeds
+
+ # Matt: ESM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
+ # flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
+ # masked tokens are treated as if they were selected for input dropout and zeroed out.
+ # This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
+ # a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
+ # This is analogous to the way that dropout layers scale down outputs during evaluation when not
+ # actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
+ if self.token_dropout:
+ embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
+ mask_ratio_train = 0.15 * 0.8 # Hardcoded as the ratio used in all ESM model training runs
+ src_lengths = attention_mask.sum(-1)
+ mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths
+ embeddings = (embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]).to(
+ embeddings.dtype
+ )
+
+ if self.position_embedding_type == "absolute":
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings = embeddings + position_embeddings
+
+ if self.layer_norm is not None:
+ embeddings = self.layer_norm(embeddings)
+ if attention_mask is not None:
+ embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype)
+ # Matt: I think this line was copied incorrectly from BERT, disabling it for now.
+ # embeddings = self.dropout(embeddings)
+ return embeddings
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class EsmSelfAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.position_embedding_type = position_embedding_type or getattr(
+ config, "position_embedding_type", "absolute"
+ )
+ self.rotary_embeddings = None
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+ self.max_position_embeddings = config.max_position_embeddings
+ self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+ elif self.position_embedding_type == "rotary":
+ self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
+
+ self.is_decoder = config.is_decoder
+
+ def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+ x = x.view(new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> Tuple[torch.Tensor]:
+ mixed_query_layer = self.query(hidden_states)
+
+ # If this is instantiated as a cross-attention module, the keys
+ # and values come from an encoder; the attention mask needs to be
+ # such that the encoder's padding tokens are not attended to.
+ is_cross_attention = encoder_hidden_states is not None
+
+ if is_cross_attention and past_key_value is not None:
+ # reuse k,v, cross_attentions
+ key_layer = past_key_value[0]
+ value_layer = past_key_value[1]
+ attention_mask = encoder_attention_mask
+ elif is_cross_attention:
+ key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+ value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+ attention_mask = encoder_attention_mask
+ elif past_key_value is not None:
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+ key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+ value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+ else:
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+
+ # Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
+ # ESM scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
+ # but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
+ # ESM code and fix rotary embeddings.
+ query_layer = query_layer * self.attention_head_size**-0.5
+
+ if self.is_decoder:
+ # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+ # Further calls to cross_attention layer can then reuse all cross-attention
+ # key/value_states (first "if" case)
+ # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
+ past_key_value = (key_layer, value_layer)
+
+ if self.position_embedding_type == "rotary":
+ query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+ seq_length = hidden_states.size()[1]
+ position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+ position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+ distance = position_ids_l - position_ids_r
+ positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+ positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
+
+ if self.position_embedding_type == "relative_key":
+ relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+ attention_scores = attention_scores + relative_position_scores
+ elif self.position_embedding_type == "relative_key_query":
+ relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+ relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+ attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in EsmModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ if self.is_decoder:
+ outputs = outputs + (past_key_value,)
+ return outputs
+
+
+class EsmSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = hidden_states + input_tensor
+ return hidden_states
+
+
+class EsmAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = EsmSelfAttention(config)
+ self.output = EsmSelfOutput(config)
+ self.pruned_heads = set()
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_value=None,
+ output_attentions=False,
+ ):
+ hidden_states_ln = self.LayerNorm(hidden_states)
+ self_outputs = self.self(
+ hidden_states_ln,
+ attention_mask,
+ head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ past_key_value,
+ output_attentions,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+class EsmIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = gelu(hidden_states)
+ return hidden_states
+
+
+class EsmOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = hidden_states + input_tensor
+ return hidden_states
+
+
+class EsmLayer(nn.Module):
+ def __init__(self, config, layer_id):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = EsmAttention(config)
+ self.is_decoder = config.is_decoder
+ self.add_cross_attention = config.add_cross_attention and (layer_id + 1) % config.adapter_freq == 0
+ if self.add_cross_attention:
+ """
+ fuck this assertion
+ if not self.is_decoder:
+ raise RuntimeError(f"{self} should be used as a decoder model if cross attention is added")
+ """
+ adapter_config = copy.deepcopy(config)
+ adapter_config.intermediate_size = adapter_config.hidden_size // 2
+ self.crossattention_adapter = EsmAttention(adapter_config)
+ self.intermediate_adapter = EsmIntermediate(adapter_config)
+ self.output_adapter = EsmOutput(adapter_config)
+ self.LayerNorm_adapter = nn.LayerNorm(adapter_config.hidden_size, adapter_config.layer_norm_eps)
+ self.intermediate = EsmIntermediate(config)
+ self.output = EsmOutput(config)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_value=None,
+ output_attentions=False,
+ ):
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ past_key_value=self_attn_past_key_value,
+ )
+ attention_output = self_attention_outputs[0]
+
+ # if decoder, the last output is tuple of self-attn cache
+ if self.is_decoder:
+ outputs = self_attention_outputs[1:-1]
+ present_key_value = self_attention_outputs[-1]
+ else:
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ layer_output = self.feed_forward_chunk(attention_output)
+
+ cross_attn_present_key_value = None
+ if self.add_cross_attention and encoder_hidden_states is not None:
+ if not hasattr(self, "crossattention_adapter"):
+ raise AttributeError(
+ f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
+ " with cross-attention layers by setting `config.add_cross_attention=True`"
+ )
+
+ # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
+ cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+ cross_attention_outputs = self.crossattention_adapter(
+ layer_output,
+ attention_mask,
+ head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ cross_attn_past_key_value,
+ output_attentions,
+ )
+ attention_output = cross_attention_outputs[0]
+ layer_output = self.feed_forward_chunk_adapter(attention_output)
+
+ outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
+
+ # add cross-attn cache to positions 3,4 of present_key_value tuple
+ # cross_attn_present_key_value = cross_attention_outputs[-1]
+ # present_key_value = present_key_value + cross_attn_present_key_value
+
+ outputs = (layer_output,) + outputs
+
+ # if decoder, return the attn key/values as the last output
+ if self.is_decoder:
+ outputs = outputs + (present_key_value,)
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ attention_output_ln = self.LayerNorm(attention_output)
+ intermediate_output = self.intermediate(attention_output_ln)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+ def feed_forward_chunk_adapter(self, attention_output):
+ attention_output_ln = self.LayerNorm_adapter(attention_output)
+ intermediate_output = self.intermediate_adapter(attention_output_ln)
+ layer_output = self.output_adapter(intermediate_output, attention_output)
+ return layer_output
+
+
+class EsmEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([EsmLayer(config, i) for i in range(config.num_hidden_layers)])
+ self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_values=None,
+ use_cache=None,
+ output_attentions=False,
+ output_hidden_states=False,
+ return_dict=True,
+ ):
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+ "`use_cache=False`..."
+ )
+ use_cache = False
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+ all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+ next_decoder_cache = () if use_cache else None
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+ past_key_value = past_key_values[i] if past_key_values is not None else None
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ layer_module.__call__,
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ past_key_value,
+ output_attentions,
+ )
+ else:
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ past_key_value,
+ output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+ if use_cache:
+ next_decoder_cache = next_decoder_cache + (layer_outputs[-1],)
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+ if self.config.add_cross_attention:
+ all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+ if self.emb_layer_norm_after:
+ hidden_states = self.emb_layer_norm_after(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ next_decoder_cache,
+ all_hidden_states,
+ all_self_attentions,
+ all_cross_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=next_decoder_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class EsmPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class EsmPreTrainedModel(PreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = EsmConfig
+ base_model_prefix = "esm"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["EsmLayer", "EsmFoldTriangularSelfAttentionBlock", "EsmEmbeddings"]
+
+ # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+ESM_START_DOCSTRING = r"""
+
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`EsmConfig`]): Model configuration class with all the parameters of the
+ model. Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+ESM_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
+ ESM_START_DOCSTRING,
+)
+class EsmModel(EsmPreTrainedModel):
+ """
+
+ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+ cross-attention is added between the self-attention layers, following the architecture described in [Attention is
+ all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+ Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+ To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
+ to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
+ `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
+ """
+
+ def __init__(self, config, add_pooling_layer=True):
+ config.add_cross_attention = True
+ config.adapter_freq = 11
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = EsmEmbeddings(config)
+ self.encoder = EsmEncoder(config)
+
+ self.pooler = EsmPooler(config) if add_pooling_layer else None
+
+ self.contact_head = EsmContactPredictionHead(
+ in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=BaseModelOutputWithPoolingAndCrossAttentions,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+ r"""
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+ the model is configured as a decoder.
+ encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+ the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.config.is_decoder:
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ else:
+ use_cache = False
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ batch_size, seq_length = input_shape
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ # past_key_values_length
+ past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+ if attention_mask is None:
+ attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+ # If a 2D or 3D attention mask is provided for the cross-attention
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if self.config.is_decoder and encoder_hidden_states is not None:
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+ if encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+ encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+ else:
+ encoder_extended_attention_mask = None
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ past_key_values_length=past_key_values_length,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_extended_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPoolingAndCrossAttentions(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ past_key_values=encoder_outputs.past_key_values,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ cross_attentions=encoder_outputs.cross_attentions,
+ )
+
+ def predict_contacts(self, tokens, attention_mask):
+ attns = self(tokens, attention_mask=attention_mask, return_dict=True, output_attentions=True).attentions
+ attns = torch.stack(attns, dim=1) # Matches the original model layout
+ # In the original model, attentions for padding tokens are completely zeroed out.
+ # This makes no difference most of the time because the other tokens won't attend to them,
+ # but it does for the contact prediction task, which takes attentions as input,
+ # so we have to mimic that here.
+ attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
+ attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
+ return self.contact_head(tokens, attns)
+
+
+@add_start_docstrings("""ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING)
+class EsmForMaskedLM(EsmPreTrainedModel):
+ _tied_weights_keys = ["lm_head.decoder.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ if config.is_decoder:
+ logger.warning(
+ "If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
+ "bi-directional self-attention."
+ )
+
+ self.esm = EsmModel(config, add_pooling_layer=False)
+ self.lm_head = EsmLMHead(config)
+
+ self.init_weights()
+
+ def get_output_embeddings(self):
+ return self.lm_head.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head.decoder = new_embeddings
+
+ @add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=MaskedLMOutput,
+ config_class=_CONFIG_FOR_DOC,
+ mask="",
+ )
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, MaskedLMOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+ Used to hide legacy arguments that have been deprecated.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.esm(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+ prediction_scores = self.lm_head(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss(reduction='none')
+
+ labels = labels.to(prediction_scores.device)
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def predict_contacts(self, tokens, attention_mask):
+ return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
+
+
+class EsmLMHead(nn.Module):
+ """ESM Head for masked language modeling."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ def forward(self, features, **kwargs):
+ x = self.dense(features)
+ x = gelu(x)
+ x = self.layer_norm(x)
+
+ # project back to size of vocabulary with bias
+ x = self.decoder(x) + self.bias
+ return x
+
+
+@add_start_docstrings(
+ """
+ ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+ output) e.g. for GLUE tasks.
+ """,
+ ESM_START_DOCSTRING,
+)
+class EsmForSequenceClassification(EsmPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.esm = EsmModel(config, add_pooling_layer=False)
+ self.classifier = EsmClassificationHead(config)
+
+ self.init_weights()
+
+ @add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=SequenceClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.esm(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+ Named-Entity-Recognition (NER) tasks.
+ """,
+ ESM_START_DOCSTRING,
+)
+class EsmForTokenClassification(EsmPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.esm = EsmModel(config, add_pooling_layer=False)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ self.init_weights()
+
+ @add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TokenClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.esm(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ attention_mask = attention_mask.to()
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+
+ labels = labels.to(logits.device)
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class EsmClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+ def forward(self, features, **kwargs):
+ x = features[:, 0, :] # take token (equiv. to [CLS])
+ x = self.dropout(x)
+ x = self.dense(x)
+ x = torch.tanh(x)
+ x = self.dropout(x)
+ x = self.out_proj(x)
+ return x
+
+class EsmForMutationDesign(EsmPreTrainedModel):
+ def __init__(self, config):
+ config.add_cross_attention = True
+ config.adapter_freq = 3
+ super().__init__(config)
+
+ self.esm = EsmModel(config, add_pooling_layer=False)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.mutation_classifier = nn.Linear(config.hidden_size, 2)
+ self.lm_head = EsmLMHead(config)
+
+ self.init_weights()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ mutation_position: Optional[torch.LongTensor] = None,
+ mutation_aa: Optional[torch.LongTensor] = None,
+ batch_idx: Optional[torch.LongTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, ModelOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.esm(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+ sequence_output = self.dropout(sequence_output)
+ mutation_pos_logits = self.mutation_classifier(sequence_output)
+ # extended_attention_mask = (1.0 - attention_mask) * torch.finfo(sequence_output.dtype).min
+ # mutation_pos_logits += extended_attention_mask
+
+ extended_mutation_position = batch_idx * sequence_output.shape[1] + mutation_position
+ mutation_aa_logits = self.lm_head(sequence_output.view(-1, sequence_output.shape[2])[extended_mutation_position])
+
+ # mutation_position_label = torch.eye(input_ids.shape[1], dtype=torch.long).to(input_ids.device)[mutation_position] - 100 * (1 - attention_mask)
+ mutation_position_label = torch.zeros(input_ids.shape, dtype=torch.long).to(input_ids.device)
+ mutation_position_label[batch_idx, mutation_position] = 1
+ mutation_position_label -= 100 * (1 - attention_mask)
+
+ loss_fct_pos = CrossEntropyLoss(weight=torch.tensor([1.0, 50.0]).to(input_ids.device))
+ loss_fct_aa = CrossEntropyLoss()
+ loss_pos = loss_fct_pos(mutation_pos_logits.view(-1, 2), mutation_position_label.view(-1))
+ loss_aa = loss_fct_aa(mutation_aa_logits.view(-1, self.config.vocab_size), mutation_aa.view(-1))
+
+ if not return_dict:
+ output = (mutation_pos_logits, mutation_aa_logits,) + outputs[2:]
+ return ((loss_pos, loss_aa) + output)
+
+ return ModelOutput(
+ loss_pos=loss_pos,
+ loss_aa=loss_aa,
+ logits_pos=mutation_pos_logits,
+ logits_aa=mutation_aa_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ @torch.no_grad()
+ def lm_design(self, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask):
+ outputs = self.esm(
+ input_ids,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=True
+ )
+ sequence_output = outputs[0]
+ mutation_pos_prob = F.softmax(self.mutation_classifier(sequence_output) / 0.5, dim=-1)[:, :, 1] * attention_mask
+ mutation_pos_prob[:, 0] = 0
+ mutation_pos_prob[input_ids == 2] = 0
+ mutation_aa_prob = F.softmax(self.lm_head(sequence_output) / 0.5, dim=-1)
+ i = torch.arange(mutation_aa_prob.shape[0]).unsqueeze(1).expand(mutation_aa_prob.shape[:-1])
+ j = torch.arange(mutation_aa_prob.shape[1]).unsqueeze(0).expand(mutation_aa_prob.shape[:-1])
+ mutation_aa_prob[i, j, input_ids] = 0
+ mutation_prob = mutation_pos_prob.unsqueeze(2) * mutation_aa_prob
+
+ return mutation_prob
+
+
+def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ x: torch.Tensor x:
+
+ Returns: torch.Tensor
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
+ return incremental_indices.long() + padding_idx
\ No newline at end of file
diff --git a/open_biomed/models/protein/mutaplm/mutaplm.py b/open_biomed/models/protein/mutaplm/mutaplm.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ae601b8b0d450ade0843af1caf3ed8d9e6f4060
--- /dev/null
+++ b/open_biomed/models/protein/mutaplm/mutaplm.py
@@ -0,0 +1,545 @@
+import contextlib
+from typing import Any, Dict, List, Optional, Tuple
+
+import logging
+import torch
+import torch.nn as nn
+
+from huggingface_hub import snapshot_download
+import os
+from transformers import PreTrainedTokenizer, LlamaTokenizer, LlamaConfig, LlamaForCausalLM, EsmTokenizer, DataCollatorWithPadding
+from peft import get_peft_model, LoraConfig, TaskType
+
+from open_biomed.data import Protein, Text
+from open_biomed.models.task_models.mutation_text_translation import MutationExplanationModel, MutationEngineeringModel
+from open_biomed.models.protein.mutaplm.modeling_esm import EsmForMutationDesign
+from open_biomed.utils.collator import Collator, ClassLabelCollator, EnsembleCollator, ListCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurizer, Featurized
+
+class MutaPLMExplanationFeaturizer(Featurizer):
+ def __init__(self,
+ protein_tokenizer: PreTrainedTokenizer,
+ text_tokenizer: PreTrainedTokenizer,
+ max_length_func: int=512,
+ max_length_protein: int=1024,
+ max_length_label: int=256,
+ stage2_prompt: str="",
+ ) -> None:
+ super().__init__()
+ self.protein_tokenizer = protein_tokenizer
+ self.text_tokenizer = text_tokenizer
+ self.max_length_func = max_length_func
+ self.max_length_protein = max_length_protein
+ self.max_length_label = max_length_label
+ self.stage2_prompt = stage2_prompt
+
+ def __call__(self, protein: Protein, mutation: str, label: Optional[Text]=None, function: Optional[Text]=None) -> Dict[str, Any]:
+ pos = int(mutation[1:-1])
+ wild_type = protein.sequence
+ mutant = wild_type[:pos - 1] + mutation[-1] + wild_type[pos:]
+ featurized = {}
+ featurized["wild_type"] = self.protein_tokenizer(
+ wild_type,
+ max_length=self.max_length_protein,
+ truncation=True,
+ add_special_tokens=True,
+ )
+ featurized["mutant"] = self.protein_tokenizer(
+ mutant,
+ max_length=self.max_length_protein,
+ truncation=True,
+ add_special_tokens=True,
+ )
+ featurized["stage2_prompt"] = self.text_tokenizer(
+ self.stage2_prompt.format(mutation[0], mutation[-1], mutation[1:-1]),
+ max_length=self.max_length_label,
+ truncation=True,
+ add_special_tokens=False,
+ )
+ if label is not None:
+ featurized["label"] = self.text_tokenizer(
+ label.str,
+ max_length=self.max_length_label,
+ truncation=True,
+ add_special_tokens=False,
+ )
+ if function is not None:
+ featurized["gt_function"] = self.text_tokenizer(
+ function.str,
+ max_length=self.max_length_func,
+ truncation=True,
+ add_special_tokens=False,
+ )
+
+ return featurized
+
+ def get_attrs(self) -> List[str]:
+ return ["wild_type", "mutant", "stage2_prompt", "label", "gt_function"]
+
+class MutaPLMEngineeringFeaturizer(Featurizer):
+ def __init__(self,
+ protein_tokenizer: PreTrainedTokenizer,
+ text_tokenizer: PreTrainedTokenizer,
+ max_length_func: int=512,
+ max_length_protein: int=1024,
+ max_length_prompt: int=256,
+ ) -> None:
+ super().__init__()
+ self.protein_tokenizer = protein_tokenizer
+ self.text_tokenizer = text_tokenizer
+ self.max_length_func = max_length_func
+ self.max_length_protein = max_length_protein
+ self.max_length_prompt = max_length_prompt
+
+ def __call__(self, protein: Protein, text: Text, label: Optional[str]=None, function: Optional[Text]=None) -> Dict[str, Any]:
+ featurized = {}
+ featurized["wild_type"] = self.protein_tokenizer(
+ protein.sequence,
+ max_length=self.max_length_protein,
+ truncation=True,
+ add_special_tokens=True,
+ )
+ featurized["prompt"] = self.text_tokenizer(
+ text.str,
+ max_length=self.max_length_prompt,
+ truncation=True,
+ add_special_tokens=False,
+ )
+ if label is not None:
+ featurized["label"] = {
+ "pos": torch.LongTensor(int(label[1:-1]) - 1),
+ "aa": self.protein_tokenizer(label[-1], return_tensors='pt').input_ids,
+ }
+ if function is not None:
+ featurized["gt_function"] = self.text_tokenizer(
+ function.str,
+ max_length=self.max_length_func,
+ truncation=True,
+ add_special_tokens=False,
+ )
+
+ return featurized
+
+ def get_attrs(self) -> List[str]:
+ return ["wild_type", "prompt", "label", "gt_function"]
+
+class MutaPLM(MutationExplanationModel, MutationEngineeringModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super(MutaPLM, self).__init__(model_cfg)
+
+ # load esm
+ logging.info("*** loading protein model...")
+ if not os.path.exists(model_cfg.protein_model):
+ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+ logging.info("Repo not found. Try downloading from snapshot")
+ snapshot_download(repo_id="facebook/esm2_t33_650M_UR50D", local_dir=model_cfg.hf_model_name_or_path, force_download=True)
+ self.protein_model = EsmForMutationDesign.from_pretrained(model_cfg.protein_model, torch_dtype=torch.bfloat16) # delta decoder is here
+ self.protein_tokenizer = EsmTokenizer.from_pretrained(model_cfg.protein_model)
+
+ # load llm
+ logging.info("*** loading llm tokenizer...")
+ if not os.path.exists(model_cfg.protein_model):
+ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+ logging.info("Repo not found. Try downloading from snapshot")
+ snapshot_download(repo_id="PharMolix/BioMedGPT-LM-7B", local_dir=model_cfg.hf_model_name_or_path, force_download=True)
+ self.llm_tokenizer = LlamaTokenizer.from_pretrained(model_cfg.llama_ckpt, truncation_side="left")
+ self.llm_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+ self.llm_tokenizer.add_special_tokens({'bos_token': ''})
+ self.llm_tokenizer.add_special_tokens({'eos_token': ''})
+ self.llm_tokenizer.add_special_tokens({'unk_token': ''})
+ logging.info(f"*** loading llm from {model_cfg.llama_ckpt}...")
+ llm_cfg = LlamaConfig.from_pretrained(model_cfg.llama_ckpt)
+ self.llm = LlamaForCausalLM(llm_cfg)
+ self.llm.resize_token_embeddings(len(self.llm_tokenizer))
+
+ # add lora
+ logging.info("*** adding LoRA...")
+ lora_config = LoraConfig(
+ peft_type=TaskType.CAUSAL_LM,
+ inference_mode=True,
+ r=16, lora_alpha=16,
+ lora_dropout=0.05,
+ target_modules=["v_proj", "k_proj", "o_proj", "gate_proj", "down_proj", "up_proj"],
+ )
+ self.llm = get_peft_model(self.llm, lora_config)
+ self.llm.print_trainable_parameters()
+
+ # delta encoder and decoder
+ logging.info("*** building delta network...")
+ self.query_protein1 = nn.Parameter(
+ torch.zeros(1, model_cfg.num_query_tokens_protein1, self.protein_model.config.hidden_size)
+ )
+ nn.init.normal_(self.query_protein1, 0, 0.02)
+ self.query_protein2 = nn.Parameter(
+ torch.zeros(1, model_cfg.num_query_tokens_protein2, self.protein_model.config.hidden_size)
+ )
+ nn.init.normal_(self.query_protein2, 0, 0.02)
+ self.pooler_protein1 = nn.MultiheadAttention(
+ embed_dim=self.protein_model.config.hidden_size,
+ num_heads=model_cfg.ca_num_head,
+ batch_first=True
+ )
+ self.pooler_protein2 = nn.MultiheadAttention(
+ embed_dim=self.protein_model.config.hidden_size,
+ num_heads=model_cfg.ca_num_head,
+ batch_first=True
+ )
+
+ self.bop_embeds = nn.Parameter(torch.zeros(1, 1, self.llm.config.hidden_size))
+ self.eop_embeds = nn.Parameter(torch.zeros(1, 1, self.llm.config.hidden_size))
+ self.bom_embeds = nn.Parameter(torch.zeros(1, 1, self.llm.config.hidden_size))
+ self.eom_embeds = nn.Parameter(torch.zeros(1, 1, self.llm.config.hidden_size))
+ self.soft_tokens = nn.Parameter(torch.zeros(1, model_cfg.num_query_tokens_protein2, self.llm.config.hidden_size))
+ nn.init.normal_(self.bop_embeds, 0, 0.02)
+ nn.init.normal_(self.eop_embeds, 0, 0.02)
+ nn.init.normal_(self.bom_embeds, 0, 0.02)
+ nn.init.normal_(self.eom_embeds, 0, 0.02)
+ nn.init.normal_(self.soft_tokens, 0, 0.02)
+
+ # build proj
+ self.proj_protein1 = nn.Linear(self.protein_model.config.hidden_size, self.llm.config.hidden_size)
+ self.proj_protein2 = nn.Linear(self.protein_model.config.hidden_size, self.llm.config.hidden_size)
+ self.proj_text = nn.Linear(self.llm.config.hidden_size, self.protein_model.config.hidden_size)
+
+ for parent in reversed(type(self).__mro__[1:-1]):
+ if hasattr(parent, '_add_task'):
+ parent._add_task(self)
+
+ def load_ckpt(self, state_dict: Dict[str, torch.Tensor]) -> None:
+ self.load_state_dict(state_dict["model"])
+
+ def featurizer_mutation_explanation(self) -> Tuple[Featurizer, Collator]:
+ return MutaPLMExplanationFeaturizer(
+ self.protein_tokenizer,
+ self.llm_tokenizer,
+ self.config.func_maxlen,
+ self.config.protein_maxlen,
+ self.config.text_maxlen,
+ self.config.stage2_prompt,
+ ), EnsembleCollator({
+ "wild_type": DataCollatorWithPadding(self.protein_tokenizer, padding=True),
+ "mutant": DataCollatorWithPadding(self.protein_tokenizer, padding=True),
+ "label": ListCollator(),
+ "gt_function": DataCollatorWithPadding(self.llm_tokenizer, padding=False),
+ "stage2_prompt": DataCollatorWithPadding(self.llm_tokenizer, padding=False),
+ })
+
+ def featurizer_mutation_engineering(self) -> Tuple[Featurizer, Collator]:
+ return MutaPLMEngineeringFeaturizer(
+ self.protein_tokenizer,
+ self.llm_tokenizer,
+ self.config.func_maxlen,
+ self.config.protein_maxlen,
+ self.config.text_maxlen,
+ ), EnsembleCollator({
+ "wild_type": DataCollatorWithPadding(self.protein_tokenizer, padding=True),
+ "prompt": DataCollatorWithPadding(self.llm_tokenizer, padding=False),
+ "label": EnsembleCollator({
+ "pos": ClassLabelCollator(),
+ "aa": ClassLabelCollator(),
+ }),
+ "gt_function": DataCollatorWithPadding(self.llm_tokenizer, padding=False),
+ })
+
+ def maybe_autocast(self, device="cuda:0", dtype=torch.bfloat16):
+ # if on cpu, don't use autocast
+ # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
+ enable_autocast = device != torch.device("cpu")
+
+ if enable_autocast:
+ return torch.cuda.amp.autocast(dtype=dtype)
+ else:
+ return contextlib.nullcontext()
+
+
+ def _encode_protein(self, protein1: Featurized[Protein], protein2: Optional[Featurized[Protein]]):
+ batch_size = protein1.input_ids.shape[0]
+ device = protein1.input_ids.device
+
+ with self.maybe_autocast(device):
+ p_feature1_in = self.protein_model.esm(**protein1) # last_hidden_states: [bs, prot_len, esm_hidden_size]
+ query_protein1 = self.query_protein1.expand(batch_size, -1, -1)
+ attn_mask_1 = (1 - protein1.attention_mask.repeat(self.config.ca_num_head, 1).unsqueeze(1).expand(-1, self.config.num_query_tokens_protein1, -1)).to(bool)
+ p_feature1 = self.pooler_protein1(
+ query_protein1,
+ p_feature1_in[0],
+ p_feature1_in[0],
+ attn_mask = attn_mask_1
+ )
+ protein1_embeds = self.proj_protein1(p_feature1[0])
+
+ if protein2 is not None:
+ p_feature2_in = self.protein_model.esm(**protein2)
+ query_protein2 = self.query_protein2.expand(batch_size, -1, -1)
+ attn_mask_2 = (1 - protein2.attention_mask.repeat(self.config.ca_num_head, 1).unsqueeze(1).expand(-1, self.config.num_query_tokens_protein2, -1)).to(bool)
+ delta_feature = p_feature2_in[0] - p_feature1_in[0]
+ p_feature2 = self.pooler_protein2(
+ query_protein2,
+ delta_feature,
+ delta_feature,
+ attn_mask = attn_mask_2
+ )
+ protein2_embeds = self.proj_protein2(p_feature2[0])
+
+ if protein2 is not None:
+ return protein1_embeds, protein2_embeds
+ else:
+ return protein1_embeds
+
+
+ def add_padding(self, wrapped_embeds: torch.Tensor, wrapped_attention_mask: torch.Tensor=None, targets: torch.Tensor=None, regress_ids: torch.Tensor=None, padding: str="right"):
+ assert (targets is None) or (regress_ids is None)
+ batch_size = len(wrapped_embeds)
+ max_length_batch = max([x.shape[1] for x in wrapped_embeds])
+ for i in range(batch_size):
+ pad_len = max_length_batch - wrapped_embeds[i].shape[1]
+ if padding == "right":
+ wrapped_embeds[i] = torch.cat((
+ wrapped_embeds[i],
+ torch.zeros((1, pad_len, wrapped_embeds[i].shape[2]), dtype=wrapped_embeds[i].dtype).to(wrapped_embeds[i].device)
+ ), dim=1)
+ if wrapped_attention_mask:
+ wrapped_attention_mask[i] = torch.cat((
+ wrapped_attention_mask[i],
+ torch.zeros((1, pad_len), dtype=wrapped_attention_mask[i].dtype).to(wrapped_attention_mask[i].device)
+ ), dim=1)
+ if targets:
+ targets[i] = torch.cat((
+ targets[i],
+ torch.ones((1, pad_len), dtype=targets[i].dtype).to(targets[i].device).fill_(-100)
+ ), dim=1)
+ if regress_ids:
+ regress_ids[i] = torch.cat((
+ regress_ids[i],
+ torch.zeros((pad_len), dtype=regress_ids[i].dtype).to(regress_ids[i].device)
+ ), dim=0)
+ else:
+ wrapped_embeds[i] = torch.cat((
+ torch.zeros((1, pad_len, wrapped_embeds[i].shape[2]), dtype=wrapped_embeds[i].dtype).to(wrapped_embeds[i].device),
+ wrapped_embeds[i],
+ ), dim=1)
+ if wrapped_attention_mask:
+ wrapped_attention_mask[i] = torch.cat((
+ torch.zeros((1, pad_len), dtype=wrapped_attention_mask[i].dtype).to(wrapped_attention_mask[i].device),
+ wrapped_attention_mask[i],
+ ), dim=1)
+ if targets:
+ targets[i] = torch.cat((
+ torch.ones((1, pad_len), dtype=targets[i].dtype).to(targets[i].device).fill_(-100),
+ targets[i],
+ ), dim=1)
+ if regress_ids:
+ regress_ids[i] = torch.cat((
+ torch.zeros((pad_len), dtype=regress_ids[i].dtype).to(regress_ids[i].device),
+ regress_ids[i]
+ ), dim=0)
+
+ if targets:
+ return torch.cat(wrapped_embeds, dim=0), torch.cat(wrapped_attention_mask, dim=0), torch.cat(targets, dim=0)
+ if regress_ids:
+ return torch.cat(wrapped_embeds, dim=0), torch.cat(wrapped_attention_mask, dim=0), torch.stack(regress_ids, dim=0)
+ if wrapped_attention_mask is None:
+ return torch.cat(wrapped_embeds, dim=0)
+ else:
+ return torch.cat(wrapped_embeds, dim=0), torch.cat(wrapped_attention_mask, dim=0)
+
+ def _wrapped_sentence_inference(
+ self,
+ protein1_embeds: torch.Tensor=None,
+ protein2_embeds: Optional[torch.Tensor]=None,
+ stage2_prompt: Featurized[Text]=None,
+ predicted_function: Optional[Featurized[Text]]=None,
+ mutation_description: Optional[Featurized[Text]]=None,
+ ):
+ batch_size = protein1_embeds.shape[0]
+ device = protein1_embeds.device
+ input_emb = self.llm.get_input_embeddings()
+ bos_tokens = self.llm_tokenizer('', return_tensors='pt', add_special_tokens=False).to(device).input_ids
+ bos_embeds = input_emb(bos_tokens) # [1, 1, 4096]
+ sys_prompt_tokens = self.llm_tokenizer(
+ self.config.system_prompt,
+ max_length=self.config.func_maxlen,
+ padding=False,
+ truncation=True,
+ return_tensors='pt',
+ add_special_tokens=False,
+ ).to(device).input_ids
+ sys_embeds = input_emb(sys_prompt_tokens)
+ if predicted_function is None: # CoT stage 1
+ sys_embeds = sys_embeds.expand(batch_size, -1, -1)
+ bos_embeds = bos_embeds.expand(batch_size, -1, -1)
+ bop_embeds = self.bop_embeds.expand(batch_size, -1, -1)
+ eop_embeds = self.eop_embeds.expand(batch_size, -1, -1)
+ bom_embeds = self.bom_embeds.expand(batch_size, -1, -1)
+ eom_embeds = self.eom_embeds.expand(batch_size, -1, -1)
+ wrapped_embeds = torch.cat([bos_embeds, sys_embeds, bop_embeds, protein1_embeds, eop_embeds], dim=1)
+ attention_mask = torch.ones((batch_size, wrapped_embeds.shape[1]), dtype=torch.long, device=device)
+ return wrapped_embeds, attention_mask
+
+ else: # CoT stage 2
+ bop_embeds = self.bop_embeds.to(device)
+ eop_embeds = self.eop_embeds.to(device)
+ bom_embeds = self.bom_embeds.to(device)
+ eom_embeds = self.eom_embeds.to(device)
+ batched_embeds, batched_attn_mask = [], []
+ if mutation_description is not None:
+ batched_regress_ids = []
+ for i in range(batch_size):
+ function_tokens = predicted_function[i]
+ func_embeds = input_emb(function_tokens)
+
+ if mutation_description is not None:
+ mut_eff_embeds = input_emb(mutation_description.input_ids[i])
+ soft_embeds = self.soft_tokens.to(device)
+ regress_start_id = sys_embeds.shape[1] + self.config.num_query_tokens_protein1 + 4 + func_embeds.shape[0] + mut_eff_embeds.shape[0]
+ wrapped_embeds = torch.cat([
+ bos_embeds, sys_embeds, bop_embeds, protein1_embeds[i].unsqueeze(0), eop_embeds,
+ func_embeds.unsqueeze(0), mut_eff_embeds.unsqueeze(0),
+ bom_embeds, soft_embeds
+ ], dim=1)
+ regress_ids = torch.cat([
+ torch.zeros(regress_start_id, dtype=torch.long, device=device),
+ torch.ones(self.config.num_query_tokens_protein2, dtype=torch.long, device=device),
+ ], dim=0).bool()
+ batched_regress_ids.append(regress_ids)
+ else:
+ mutation_tokens = stage2_prompt.input_ids[i]
+ muta_embeds = input_emb(mutation_tokens)
+ wrapped_embeds = torch.cat([
+ bos_embeds, sys_embeds, bop_embeds, protein1_embeds[i].unsqueeze(0), eop_embeds,
+ func_embeds.unsqueeze(0), muta_embeds.unsqueeze(0),
+ bom_embeds, protein2_embeds[i].unsqueeze(0), eom_embeds,
+ ], dim=1)
+ wrapped_attn_mask = torch.ones((1, wrapped_embeds.shape[1]), dtype=torch.long, device=device)
+ batched_embeds.append(wrapped_embeds)
+ batched_attn_mask.append(wrapped_attn_mask)
+
+ if mutation_description is None:
+ batched_embeds, batched_attn_mask = self.add_padding(
+ batched_embeds, batched_attn_mask, targets=None, regress_ids=None, padding="left")
+ return batched_embeds, batched_attn_mask
+ else:
+ batched_embeds, batched_attn_mask, batched_regress_ids = self.add_padding(
+ batched_embeds, batched_attn_mask, targets=None, regress_ids=batched_regress_ids, padding="left")
+ return batched_embeds, batched_attn_mask, batched_regress_ids
+
+ def forward_mutation_explanation(self, wild_type: Featurized[Protein], mutant: Featurized[Any], label: Featurized[Text], **kwargs) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError("Training MutaPLM is currently unavailable! Please see https://github.com/PharMolix/MutaPLM for training or use MutaPLM-mini-M2T instead.")
+
+ @torch.no_grad()
+ def predict_mutation_explanation(self,
+ wild_type: Featurized[Protein],
+ mutant: Featurized[Any],
+ stage2_prompt: Featurized[Text],
+ gt_function: Optional[Featurized[Text]]=None,
+ **kwargs
+ ) -> List[Text]:
+ device = wild_type.input_ids.device
+ with self.maybe_autocast(device):
+ protein1_embeds, protein2_embeds = self._encode_protein(wild_type, mutant)
+ if gt_function is None:
+ # stage 1
+ input_embeds, attn_mask = self._wrapped_sentence_inference(protein1_embeds, protein2_embeds)
+ outputs_function = self.llm.generate(
+ inputs_embeds=input_embeds,
+ attention_mask=attn_mask,
+ eos_token_id=self.llm_tokenizer.eos_token_id,
+ pad_token_id=self.llm_tokenizer.pad_token_id,
+ **self.config.text_generation.todict(),
+ )
+ outputs_function[outputs_function == 0] = 2 # convert output id 0 to 2 (eos_token_id)
+ outputs_function = self.llm_tokenizer.batch_decode(outputs_function)
+ logging.info(f"Predicted function: {outputs_function}")
+ outputs_function = self.llm_tokenizer(
+ outputs_function,
+ max_length=self.config.func_maxlen,
+ padding=False,
+ truncation=True,
+ return_tensors='pt',
+ add_special_tokens=False,
+ ).to(device)
+ else:
+ outputs_function = gt_function
+ # stage 2
+ input_embeds, attn_mask = self._wrapped_sentence_inference(protein1_embeds, protein2_embeds, stage2_prompt, predicted_function=outputs_function.input_ids)
+ outputs_effect = self.llm.generate(
+ inputs_embeds=input_embeds,
+ attention_mask=attn_mask,
+ eos_token_id=self.llm_tokenizer.eos_token_id,
+ pad_token_id=self.llm_tokenizer.pad_token_id,
+ **self.config.text_generation.todict(),
+ max_new_tokens=512
+ )
+ outputs_effect[outputs_effect == 0] = 2 # convert output id 0 to 2 (eos_token_id)
+ output_effect_text = self.llm_tokenizer.batch_decode(outputs_effect, skip_special_tokens=True)
+ output_effect_text = [text.strip() for text in output_effect_text]
+ return output_effect_text
+
+ def forward_mutation_engineering(self, wild_type: Featurized[Protein], prompt: Featurized[Text], label: Featurized[Any], **kwargs) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError("Training MutaPLM is currently unavailable! Please see https://github.com/PharMolix/MutaPLM for training or use MutaPLM-mini-T2M instead.")
+
+ @torch.no_grad()
+ def predict_mutation_engineering(self,
+ wild_type: Featurized[Protein],
+ prompt: Featurized[Text],
+ position: Optional[torch.LongTensor]=None,
+ gt_function: Optional[Featurized[Text]]=None,
+ **kwargs
+ ) -> List[List[str]]:
+ device = wild_type.input_ids.device
+ with self.maybe_autocast(device):
+ protein_embeds = self._encode_protein(wild_type, None)
+ if gt_function is None:
+ # stage 1
+ input_embeds, attn_mask = self._wrapped_sentence_inference(protein1_embeds=protein_embeds)
+ outputs_function = self.llm.generate(
+ inputs_embeds=input_embeds,
+ attention_mask=attn_mask,
+ eos_token_id=self.llm_tokenizer.eos_token_id,
+ pad_token_id=self.llm_tokenizer.pad_token_id,
+ **self.config.text_generation.todict(),
+ )
+ outputs_function[outputs_function == 0] = 2 # convert output id 0 to 2 (eos_token_id)
+ outputs_function = self.llm_tokenizer.batch_decode(outputs_function)
+ logging.info(f"Predicted function: {outputs_function}")
+ outputs_function = self.llm_tokenizer(
+ outputs_function,
+ max_length=self.config.func_maxlen,
+ padding=False,
+ truncation=True,
+ return_tensors='pt',
+ add_special_tokens=False,
+ ).to(device)
+ else:
+ outputs_function = gt_function
+
+ # stage 2
+ input_embeds, attn_mask, soft_ids = self._wrapped_sentence_inference(protein1_embeds=protein_embeds, predicted_function=outputs_function.input_ids, mutation_description=prompt)
+ soft_output = self.llm.model(
+ inputs_embeds=input_embeds,
+ attention_mask=attn_mask,
+ output_hidden_states=True,
+ return_dict=True
+ ).hidden_states[-1]
+ soft_output = soft_output[soft_ids].contiguous()
+ soft_output = self.proj_text(soft_output.view(wild_type.input_ids.shape[0], self.config.num_query_tokens_protein2, -1))
+ scores = self.protein_model.lm_design(
+ input_ids=wild_type.input_ids,
+ attention_mask=wild_type.attention_mask,
+ encoder_hidden_states=soft_output,
+ encoder_attention_mask=torch.ones(soft_output.shape[:-1], dtype=torch.long).to(device)
+ )
+ outputs = []
+ for i in range(scores.shape[0]):
+ if position is None:
+ top50 = scores[i].flatten().topk(50).indices
+ pred_pos = top50 // len(self.protein_tokenizer)
+ pred_aa = top50 % len(self.protein_tokenizer)
+ else:
+ pred_pos = position[i].repeat(len(self.protein_tokenizer))
+ pred_aa = scores[i][position[i]].sort(descending=True).indices
+ wt_aa = self.protein_tokenizer.batch_decode(wild_type.input_ids[i][pred_pos])
+ pred_aa = self.protein_tokenizer.batch_decode(pred_aa)
+ outputs.append([f"{wt_aa[j]}{pred_pos[j]}{pred_aa[j]}" for j in range(50)])
+ return outputs
\ No newline at end of file
diff --git a/open_biomed/models/protein/progen/modeling_progen.py b/open_biomed/models/protein/progen/modeling_progen.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc410c2aaeeeedc343c7436da92264bdb213ba9d
--- /dev/null
+++ b/open_biomed/models/protein/progen/modeling_progen.py
@@ -0,0 +1,759 @@
+# coding=utf-8
+# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Modified forward-pass implementation based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py
+
+from typing import Tuple
+
+import numpy as np
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
+
+
+logger = logging.get_logger(__name__)
+
+class ProGenConfig(PretrainedConfig):
+ model_type = "progen"
+
+ def __init__(
+ self,
+ vocab_size=50400,
+ n_positions=2048,
+ n_ctx=2048,
+ n_embd=4096,
+ n_layer=28,
+ n_head=16,
+ rotary_dim=64,
+ n_inner=None,
+ activation_function="gelu_new",
+ resid_pdrop=0.0,
+ embd_pdrop=0.0,
+ attn_pdrop=0.0,
+ layer_norm_epsilon=1e-5,
+ initializer_range=0.02,
+ scale_attn_weights=True,
+ gradient_checkpointing=False,
+ use_cache=True,
+ bos_token_id=50256,
+ eos_token_id=50256,
+ **kwargs
+ ):
+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+ self.vocab_size = vocab_size
+ self.n_ctx = n_ctx
+ self.n_positions = n_positions
+ self.n_embd = n_embd
+ self.n_layer = n_layer
+ self.n_head = n_head
+ self.n_inner = n_inner
+ self.rotary_dim = rotary_dim
+ self.activation_function = activation_function
+ self.resid_pdrop = resid_pdrop
+ self.embd_pdrop = embd_pdrop
+ self.attn_pdrop = attn_pdrop
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.initializer_range = initializer_range
+ self.gradient_checkpointing = gradient_checkpointing
+ self.scale_attn_weights = scale_attn_weights
+ self.use_cache = use_cache
+
+ self.bos_token_id = bos_token_id
+ self.eos_token_id = eos_token_id
+
+ @property
+ def max_position_embeddings(self):
+ return self.n_positions
+
+ @property
+ def hidden_size(self):
+ return self.n_embd
+
+ @property
+ def num_attention_heads(self):
+ return self.n_head
+
+ @property
+ def num_hidden_layers(self):
+ return self.n_layer
+
+
+def fixed_pos_embedding(x, seq_dim=1, seq_len=None):
+ dim = x.shape[-1]
+ if seq_len is None:
+ seq_len = x.shape[seq_dim]
+ inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
+ sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(seq_len), inv_freq).to(x.device).float()
+ return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)
+
+
+def rotate_every_two(x):
+ x1 = x[:, :, :, ::2]
+ x2 = x[:, :, :, 1::2]
+ x = torch.stack((-x2, x1), axis=-1)
+ return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')
+
+
+def apply_rotary_pos_emb(x, sincos, offset=0):
+ sin, cos = map(lambda t: t[None, offset : x.shape[1] + offset, None, :].repeat_interleave(2, 3), sincos)
+ # einsum notation for lambda t: repeat(t[offset:x.shape[1]+offset,:], "n d -> () n () (d j)", j=2)
+ return (x * cos) + (rotate_every_two(x) * sin)
+
+
+class ProGenAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ max_positions = config.max_position_embeddings
+ self.register_buffer(
+ "bias",
+ torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
+ 1, 1, max_positions, max_positions
+ ),
+ )
+ self.register_buffer("masked_bias", torch.tensor(-1e9))
+
+ self.attn_dropout = nn.Dropout(config.attn_pdrop)
+ self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+ self.embed_dim = config.hidden_size
+ self.num_attention_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_attention_heads
+ if self.head_dim * self.num_attention_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and `num_attention_heads`: {self.num_attention_heads})."
+ )
+ self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
+ self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)
+
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
+ self.rotary_dim = None
+ if config.rotary_dim is not None:
+ self.rotary_dim = config.rotary_dim
+
+ def _split_heads(self, x, n_head, dim_head, mp_num):
+ reshaped = x.reshape(x.shape[:-1] + (n_head//mp_num, dim_head))
+ reshaped = reshaped.reshape(x.shape[:-2] + (-1, ) + reshaped.shape[-1:])
+ return reshaped
+
+ def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
+ """
+ Merges attn_head_size dim and num_attn_heads dim into n_ctx
+ """
+ if len(tensor.shape) == 5:
+ tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
+ elif len(tensor.shape) == 4:
+ tensor = tensor.permute(0, 2, 1, 3).contiguous()
+ else:
+ raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
+ new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
+ return tensor.view(new_shape)
+
+ def _attn(
+ self,
+ query,
+ key,
+ value,
+ attention_mask=None,
+ head_mask=None,
+ ):
+
+ # compute causal mask from causal mask buffer
+ query_length, key_length = query.size(-2), key.size(-2)
+ causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
+
+ # Keep the attention weights computation in fp32 to avoid overflow issues
+ query = query.to(torch.float32)
+ key = key.to(torch.float32)
+
+ attn_weights = torch.matmul(query, key.transpose(-1, -2))
+
+ attn_weights = attn_weights / self.scale_attn
+ attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))
+
+ if attention_mask is not None:
+ # Apply the attention mask
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.Softmax(dim=-1)(attn_weights)
+ attn_weights = attn_weights.to(value.dtype)
+ attn_weights = self.attn_dropout(attn_weights)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask
+
+ attn_output = torch.matmul(attn_weights, value)
+
+ return attn_output, attn_weights
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ layer_past=None,
+ head_mask=None,
+ use_cache=False,
+ output_attentions=False,
+ ):
+
+ qkv = self.qkv_proj(hidden_states)
+ # TODO(enijkamp): factor out number of logical TPU-v3/v4 cores or make forward pass agnostic
+ # mp_num = 4
+ mp_num = 8
+ qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
+
+ local_dim = self.head_dim * self.num_attention_heads // mp_num
+ query, value, key = torch.split(qkv_split, local_dim, dim=-1)
+ query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
+ key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)
+
+ value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
+ value = value.permute(0, 2, 1, 3)
+
+ seq_len = key.shape[1]
+ offset = 0
+
+ if layer_past is not None:
+ offset = layer_past[0].shape[-2]
+ seq_len += offset
+
+ if self.rotary_dim is not None:
+ k_rot = key[:, :, :, : self.rotary_dim]
+ k_pass = key[:, :, :, self.rotary_dim :]
+
+ q_rot = query[:, :, :, : self.rotary_dim]
+ q_pass = query[:, :, :, self.rotary_dim :]
+
+ sincos = fixed_pos_embedding(k_rot, 1, seq_len=seq_len)
+ k_rot = apply_rotary_pos_emb(k_rot, sincos, offset=offset)
+ q_rot = apply_rotary_pos_emb(q_rot, sincos, offset=offset)
+
+ key = torch.cat([k_rot, k_pass], dim=-1)
+ query = torch.cat([q_rot, q_pass], dim=-1)
+ else:
+ sincos = fixed_pos_embedding(key, 1, seq_len=seq_len)
+ key = apply_rotary_pos_emb(key, sincos, offset=offset)
+ query = apply_rotary_pos_emb(query, sincos, offset=offset)
+
+ key = key.permute(0, 2, 1, 3)
+ query = query.permute(0, 2, 1, 3)
+
+ if layer_past is not None:
+ past_key = layer_past[0]
+ past_value = layer_past[1]
+ key = torch.cat((past_key, key), dim=-2)
+ value = torch.cat((past_value, value), dim=-2)
+
+ if use_cache is True:
+ present = (key, value)
+ else:
+ present = None
+
+ # compute self-attention: V x Softmax(QK^T)
+ attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
+
+ attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
+
+ attn_output = self.out_proj(attn_output)
+ attn_output = self.resid_dropout(attn_output)
+
+ outputs = (attn_output, present)
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs # a, present, (attentions)
+
+
+class ProGenMLP(nn.Module):
+ def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
+ super().__init__()
+ embed_dim = config.n_embd
+
+ self.fc_in = nn.Linear(embed_dim, intermediate_size)
+ self.fc_out = nn.Linear(intermediate_size, embed_dim)
+
+ self.act = ACT2FN[config.activation_function]
+ self.dropout = nn.Dropout(config.resid_pdrop)
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc_in(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.fc_out(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ return hidden_states
+
+
+class ProGenBlock(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
+ self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+ self.attn = ProGenAttention(config)
+ self.mlp = ProGenMLP(inner_dim, config)
+
+ def forward(
+ self,
+ hidden_states,
+ layer_past=None,
+ attention_mask=None,
+ head_mask=None,
+ use_cache=False,
+ output_attentions=False,
+ ):
+ residual = hidden_states
+ hidden_states = self.ln_1(hidden_states)
+ attn_outputs = self.attn(
+ hidden_states,
+ layer_past=layer_past,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ )
+ attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
+ outputs = attn_outputs[1:]
+
+ feed_forward_hidden_states = self.mlp(hidden_states)
+ hidden_states = attn_output + feed_forward_hidden_states + residual
+
+ if use_cache:
+ outputs = (hidden_states,) + outputs
+ else:
+ outputs = (hidden_states,) + outputs[1:]
+
+ return outputs # hidden_states, present, (attentions)
+
+
+class ProGenPreTrainedModel(PreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = ProGenConfig
+ base_model_prefix = "transformer"
+ is_parallelizable = True
+
+ def __init__(self, *inputs, **kwargs):
+ super().__init__(*inputs, **kwargs)
+
+ def _init_weights(self, module):
+ """Initialize the weights."""
+ if isinstance(module, (nn.Linear,)):
+ # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+class ProGenModel(ProGenPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.embed_dim = config.n_embd
+ self.vocab_size = config.vocab_size
+ self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
+ self.drop = nn.Dropout(config.embd_pdrop)
+ self.h = nn.ModuleList([ProGenBlock(config) for _ in range(config.n_layer)])
+ self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+ self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads)
+ self.init_weights()
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+
+
+ def parallelize(self, device_map=None):
+ # Check validity of device_map
+ self.device_map = (
+ get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map
+ )
+ assert_device_map(self.device_map, len(self.h))
+ self.model_parallel = True
+ self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
+ self.last_device = "cuda:" + str(max(self.device_map.keys()))
+ self.wte = self.wte.to(self.first_device)
+ # Load onto devices
+ for k, v in self.device_map.items():
+ for block in v:
+ cuda_device = "cuda:" + str(k)
+ self.h[block] = self.h[block].to(cuda_device)
+ # ln_f to last
+ self.ln_f = self.ln_f.to(self.last_device)
+
+
+ def deparallelize(self):
+ self.model_parallel = False
+ self.device_map = None
+ self.first_device = "cpu"
+ self.last_device = "cpu"
+ self.wte = self.wte.to("cpu")
+ for index in range(len(self.h)):
+ self.h[index] = self.h[index].to("cpu")
+ self.ln_f = self.ln_f.to("cpu")
+ torch.cuda.empty_cache()
+
+ def get_input_embeddings(self):
+ return self.wte
+
+ def set_input_embeddings(self, new_embeddings):
+ self.wte = new_embeddings
+
+ def forward(
+ self,
+ input_ids=None,
+ past_key_values=None,
+ attention_mask=None,
+ token_type_ids=None,
+ position_ids=None,
+ head_mask=None,
+ inputs_embeds=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ batch_size = input_ids.shape[0]
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ batch_size = inputs_embeds.shape[0]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if token_type_ids is not None:
+ token_type_ids = token_type_ids.view(-1, input_shape[-1])
+
+ if position_ids is not None:
+ position_ids = position_ids.view(-1, input_shape[-1])
+
+ if past_key_values is None:
+ past_length = 0
+ past_key_values = tuple([None] * len(self.h))
+ else:
+ past_length = past_key_values[0][0].size(-2)
+
+ if position_ids is None:
+ position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
+ position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+
+ # Attention mask.
+ if attention_mask is not None:
+ assert batch_size > 0, "batch_size has to be defined and > 0"
+ attention_mask = attention_mask.view(batch_size, -1)
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ attention_mask = attention_mask[:, None, None, :]
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
+ attention_mask = (1.0 - attention_mask) * -10000.0
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x num_attention_heads x N x N
+ # head_mask has shape n_layer x batch x num_attention_heads x N x N
+ head_mask = self.get_head_mask(head_mask, self.config.n_layer)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.wte(input_ids)
+
+ hidden_states = inputs_embeds
+
+ if token_type_ids is not None:
+ token_type_embeds = self.wte(token_type_ids)
+ hidden_states = hidden_states + token_type_embeds
+
+ hidden_states = self.drop(hidden_states)
+
+ output_shape = input_shape + (hidden_states.size(-1),)
+
+ presents = () if use_cache else None
+ all_self_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+ for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+
+ # Model parallel
+ if self.model_parallel:
+ torch.cuda.set_device(hidden_states.device)
+ # Ensure layer_past is on same device as hidden_states (might not be correct)
+ if layer_past is not None:
+ layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
+ # Ensure that attention_mask is always on the same device as hidden_states
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(hidden_states.device)
+ if isinstance(head_mask, torch.Tensor):
+ head_mask = head_mask.to(hidden_states.device)
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if getattr(self.config, "gradient_checkpointing", False) and self.training:
+
+ if use_cache:
+ logger.warning(
+ "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+ "`use_cache=False`..."
+ )
+ use_cache = False
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ # None for past_key_value
+ return module(*inputs, use_cache, output_attentions)
+
+ return custom_forward
+
+ outputs = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(block),
+ hidden_states,
+ None,
+ attention_mask,
+ head_mask[i],
+ )
+ else:
+ outputs = block(
+ hidden_states,
+ layer_past=layer_past,
+ attention_mask=attention_mask,
+ head_mask=head_mask[i],
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = outputs[0]
+ if use_cache is True:
+ presents = presents + (outputs[1],)
+
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
+
+ # Model Parallel: If it's the last layer for that device, put things on the next device
+ if self.model_parallel:
+ for k, v in self.device_map.items():
+ if i == v[-1] and "cuda:" + str(k) != self.last_device:
+ hidden_states = hidden_states.to("cuda:" + str(k + 1))
+
+ hidden_states = self.ln_f(hidden_states)
+
+ hidden_states = hidden_states.view(*output_shape)
+ # Add last hidden state
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=presents,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+class ProGenForCausalLM(ProGenPreTrainedModel):
+ _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias", r"lm_head\.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.transformer = ProGenModel(config)
+ self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
+ self.init_weights()
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+
+ def parallelize(self, device_map=None):
+ self.device_map = (
+ get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
+ if device_map is None
+ else device_map
+ )
+ assert_device_map(self.device_map, len(self.transformer.h))
+ self.transformer.parallelize(self.device_map)
+ self.lm_head = self.lm_head.to(self.transformer.first_device)
+ self.model_parallel = True
+
+ def deparallelize(self):
+ self.transformer.deparallelize()
+ self.transformer = self.transformer.to("cpu")
+ self.lm_head = self.lm_head.to("cpu")
+ self.model_parallel = False
+ torch.cuda.empty_cache()
+
+ def get_output_embeddings(self):
+ return None
+
+ def set_output_embeddings(self, new_embeddings):
+ return
+
+ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
+ token_type_ids = kwargs.get("token_type_ids", None)
+ # only last token for inputs_ids if past is defined in kwargs
+ # print(past_key_values)
+ if past_key_values:
+ input_ids = input_ids[:, -1].unsqueeze(-1)
+ if token_type_ids is not None:
+ token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+ attention_mask = kwargs.get("attention_mask", None)
+ position_ids = kwargs.get("position_ids", None)
+
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -1].unsqueeze(-1)
+ else:
+ position_ids = None
+
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update({
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "position_ids": position_ids,
+ "attention_mask": attention_mask,
+ "token_type_ids": token_type_ids,
+ })
+ return model_inputs
+
+ def forward(
+ self,
+ input_ids=None,
+ past_key_values=None,
+ attention_mask=None,
+ token_type_ids=None,
+ position_ids=None,
+ head_mask=None,
+ inputs_embeds=None,
+ labels=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
+ ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.transformer(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+
+ # Set device for model parallelism
+ if self.model_parallel:
+ torch.cuda.set_device(self.transformer.first_device)
+ hidden_states = hidden_states.to(self.lm_head.weight.device)
+
+ # make sure sampling in fp16 works correctly and
+ # compute loss in fp32 to match with mesh-tf version
+ # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
+ lm_logits = self.lm_head(hidden_states).to(torch.float32)
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = lm_logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+ loss = loss.to(hidden_states.dtype)
+
+ if not return_dict:
+ output = (lm_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ @staticmethod
+ def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
+ """
+ This function is used to re-order the :obj:`past_key_values` cache if
+ :meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is
+ called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
+ """
+ return tuple(
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
+ for layer_past in past
+ )
\ No newline at end of file
diff --git a/open_biomed/models/protein/progen/progen.py b/open_biomed/models/protein/progen/progen.py
new file mode 100644
index 0000000000000000000000000000000000000000..12b94c5a70fc2186e09af5c53cefa2db41ab2ddf
--- /dev/null
+++ b/open_biomed/models/protein/progen/progen.py
@@ -0,0 +1,178 @@
+from typing import Any, Dict, List, Optional, Tuple
+
+import contextlib
+from huggingface_hub import snapshot_download
+import logging
+import numpy as np
+import os
+import torch
+from tokenizers import Tokenizer
+from transformers import DataCollatorWithPadding
+
+from open_biomed.data import Protein
+from open_biomed.models.functional_model import ProteinDecoder
+from open_biomed.models.protein.progen.modeling_progen import ProGenForCausalLM
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurized, ProteinFeaturizer, ProteinTransformersFeaturizer
+
+class ProGenFeaturizer(ProteinFeaturizer):
+ def __init__(self,
+ tokenizer: Tokenizer,
+ max_length: int=1024,
+ ) -> None:
+ super().__init__()
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ def __call__(self, protein: Protein) -> Dict[str, Any]:
+ seq = protein.sequence
+ if len(seq) > self.max_length - 2:
+ seq = seq[:self.max_length - 2]
+ seq = '1' + seq + '2'
+ return {
+ "input_ids": self.tokenizer.encode(seq).ids,
+ "attention_mask": [1 for i in range(len(seq))],
+ }
+
+class ProGenCollator(Collator):
+ def __init__(self, tokenizer: Tokenizer) -> None:
+ super().__init__()
+ self.tokenizer = tokenizer
+ self.pad_token = self.tokenizer.encode('<|pad|>').ids[0]
+
+ def __call__(self, protein: List[Any]) -> Dict[str, torch.Tensor]:
+ max_length = np.max([len(x["input_ids"]) for x in protein])
+ input_ids, attention_mask = [], []
+ for x in protein:
+ input_ids.append(torch.LongTensor(x["input_ids"] + [self.pad_token for i in range(max_length - len(x["input_ids"]))]))
+ attention_mask.append(torch.LongTensor(x["attention_mask"] + [0 for i in range(max_length - len(x["input_ids"]))]))
+ return {
+ "input_ids": torch.stack(input_ids, dim=0),
+ "attention_mask": torch.stack(attention_mask, dim=0),
+ }
+
+class ProGen(ProteinDecoder):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+ if not os.path.exists(model_cfg.protein_model):
+ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+ logging.info("Repo not found. Try downloading from snapshot")
+ snapshot_download(repo_id="hugohrban/progen2-base", local_dir=model_cfg.hf_model_name_or_path, force_download=True)
+ self.model = ProGenForCausalLM.from_pretrained(model_cfg.model_name_or_path)
+ if getattr(model_cfg, "fp16", False):
+ self.model = self.model.to(torch.bfloat16)
+ with open(os.path.join(model_cfg.model_name_or_path, "tokenizer.json"), "r") as f:
+ self.tokenizer = Tokenizer.from_str(f.read())
+ self.featurizer = ProGenFeaturizer(self.tokenizer, max_length=model_cfg.max_length)
+ self.collator = ProGenCollator(self.tokenizer)
+
+ def get_protein_processor(self) -> Tuple[ProteinFeaturizer, Collator]:
+ return self.featurizer, self.collator
+
+ def maybe_autocast(self, dtype=torch.bfloat16):
+ # if on cpu, don't use autocast
+ # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
+ enable_autocast = self.model.device != torch.device("cpu")
+
+ if enable_autocast:
+ return torch.cuda.amp.autocast(dtype=dtype)
+ else:
+ return contextlib.nullcontext()
+
+ def freeze_parameters(self):
+ for name, param in self.named_parameters():
+ # if not (("25" in name) or ("26" in name)):
+ param.requires_grad = False
+
+ def truncate(self, sample, terminals):
+ pos = []
+ for terminal in terminals:
+ find_pos = sample.find(terminal, 1)
+ if find_pos != -1:
+ pos.append(find_pos)
+ if len(pos) > 0:
+ return sample[:(min(pos)+1)]
+ else:
+ return sample
+
+ def generate_loss(self,
+ label: Featurized[Protein],
+ add_embeds: Optional[torch.Tensor]=None, # For conditional generation (e.g. natural language)
+ add_attention_mask: Optional[torch.Tensor]=None,
+ **kwargs
+ ) -> Dict[str, torch.Tensor]:
+ with self.maybe_autocast():
+ if add_embeds is not None:
+ # Conditional generation
+ protein_embeds = self.model.get_input_embeddings()(label["input_ids"])
+ # Concatenate multiple tokenized results by putting the non-padding tokens together
+ batch_size = protein_embeds.shape[0]
+ embeds = torch.cat([add_embeds, protein_embeds], dim=1)
+ attention_mask = torch.cat([add_attention_mask, label["attention_mask"]], dim=-1)
+ non_padding_length = attention_mask.sum(-1)
+ max_length = non_padding_length.max().item()
+
+ new_embeds = []
+ new_attention_mask = []
+ new_labels = []
+ for i in range(batch_size):
+ perm = torch.cat([
+ torch.where(attention_mask[i] == 1)[0], # non-padding tokens
+ torch.where(attention_mask[i] == 0)[0], # padding tokens
+ ])
+ new_embeds.append(embeds[i][perm[:max_length]])
+ new_attention_mask.append(attention_mask[i][perm[:max_length]])
+ new_labels.append(torch.cat([
+ torch.ones(add_attention_mask[i].sum().item()).to(add_attention_mask) * -100,
+ label["input_ids"][i][torch.where(label["attention_mask"][i] == 1)],
+ torch.ones(max_length - attention_mask[i].sum().item()).to(add_attention_mask) * -100
+ ], dim=0))
+ # print(new_labels)
+ return {"loss": self.model(
+ inputs_embeds=torch.stack(new_embeds, dim=0),
+ attention_mask=torch.stack(new_attention_mask, dim=0),
+ labels=torch.stack(new_labels, dim=0),
+ return_dict=True,
+ ).loss}
+ else:
+ label_tokens = label["input_ids"].clone()
+ label_tokens[torch.where(label["attention_mask"] == 0)] = -100
+ return {"loss": self.model(
+ input_ids=label["input_ids"],
+ attention_mask=label["attention_mask"],
+ labels=label["input_ids"],
+ return_dict=True,
+ ).loss}
+
+ @torch.no_grad()
+ def generate_protein(self, add_embeds: Optional[torch.Tensor]=None, add_attention_mask: Optional[torch.Tensor]=None, **kwargs) -> List[Protein]:
+ with self.maybe_autocast():
+ if add_embeds is not None:
+ inputs = torch.ones((add_embeds.shape[0], 1), dtype=torch.long).to(self.model.device) * self.tokenizer.encode("1").ids[0]
+ inputs_embeds = self.model.get_input_embeddings()(inputs)
+ # Move padding to left
+ new_embeds, new_attention_mask = [], []
+ for i in range(add_embeds.shape[0]):
+ perm = torch.cat([
+ torch.where(add_attention_mask[i] == 1)[0], # non-padding tokens
+ torch.where(add_attention_mask[i] == 0)[0], # padding tokens
+ ])
+ new_embeds.append(torch.cat([add_embeds[i][perm], inputs_embeds[i]], dim=0))
+ new_attention_mask.append(torch.cat([add_attention_mask[i][perm], torch.ones(1, dtype=torch.long, device=self.model.device)], dim=0))
+ outputs = self.model.generate(
+ inputs_embeds=torch.stack(new_embeds, dim=0),
+ attention_mask=torch.stack(new_attention_mask, dim=0),
+ pad_token_id=self.tokenizer.encode('<|pad|>').ids[0],
+ **self.config.generation.todict(),
+ )
+ else:
+ inputs = torch.tensor(self.tokenizer.encode("1").ids).to(self.model.device)
+ outputs = self.model.generate(
+ inputs.view([1, -1]),
+ pad_token_id=self.tokenizer.encode('<|pad|>').ids[0],
+ **self.config.generation.todict()
+ )
+ as_lists = lambda batch: [batch[i, ...].detach().cpu().numpy().tolist() for i in range(batch.shape[0])]
+ outputs = self.tokenizer.decode_batch(as_lists(outputs))
+ return [Protein.from_fasta(self.truncate(output, ['1', '2'])[:-1]) for output in outputs]
\ No newline at end of file
diff --git a/open_biomed/models/task_models/__init__.py b/open_biomed/models/task_models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9be13115ae469b0361e72c97eba9138d81858590
--- /dev/null
+++ b/open_biomed/models/task_models/__init__.py
@@ -0,0 +1,10 @@
+from open_biomed.models.task_models.text_based_molecule_editing import *
+from open_biomed.models.task_models.molecule_captioning import *
+from open_biomed.models.task_models.text_guided_molecule_generation import *
+from open_biomed.models.task_models.molecule_question_answering import *
+from open_biomed.models.task_models.protein_question_answering import *
+from open_biomed.models.task_models.structure_based_drug_design import *
+from open_biomed.models.task_models.protein_molecule_docking import *
+from open_biomed.models.task_models.protein_text_translation import *
+from open_biomed.models.task_models.molecule_property_prediction import *
+from open_biomed.models.task_models.protein_folding import *
diff --git a/open_biomed/models/task_models/cell_annotation.py b/open_biomed/models/task_models/cell_annotation.py
new file mode 100644
index 0000000000000000000000000000000000000000..36dd95dd48c3701054d46a2c3dc62a20c7ec1c43
--- /dev/null
+++ b/open_biomed/models/task_models/cell_annotation.py
@@ -0,0 +1,40 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+
+from open_biomed.data import Cell, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurizer, Featurized
+
+class CellAnnotation(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ featurizer, collator = self.get_featurizer()
+ self.supported_tasks["cell_annotation"] = {
+ "forward_fn": self.forward,
+ "predict_fn": self.predict,
+ "featurizer": featurizer,
+ "collator": collator,
+ }
+
+ @abstractmethod
+ def forward(self) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict(self,
+ cell: Featurized[Cell],
+ class_texts: Featurized[Text],
+ **kwargs,
+ ) -> int:
+ raise NotImplementedError
+
+ @abstractmethod
+ def get_featurizer(self) -> Tuple[Featurizer, Collator]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/molecule_captioning.py b/open_biomed/models/task_models/molecule_captioning.py
new file mode 100644
index 0000000000000000000000000000000000000000..66fd355072f0e2124bcba8177a471bbe45d2ec63
--- /dev/null
+++ b/open_biomed/models/task_models/molecule_captioning.py
@@ -0,0 +1,43 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import EnsembleFeaturizer
+from open_biomed.utils.misc import sub_dict
+
+class MoleculeCaptioningModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["molecule_captioning"] = {
+ "forward_fn": self.forward_molecule_captioning,
+ "predict_fn": self.predict_molecule_captioning,
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["molecule"]),
+ "label": self.featurizers["text"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["molecule"]),
+ "label": self.collators["text"]
+ })
+ }
+
+ @abstractmethod
+ def forward_molecule_captioning(self,
+ molecule: List[Molecule],
+ label: List[Text],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_molecule_captioning(self,
+ molecule: List[Molecule],
+ ) -> List[Text]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/molecule_property_prediction.py b/open_biomed/models/task_models/molecule_property_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..7da1e9d7bd4c94ad2a873a6cd3fbb3d5a23dac84
--- /dev/null
+++ b/open_biomed/models/task_models/molecule_property_prediction.py
@@ -0,0 +1,67 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import EnsembleFeaturizer
+from open_biomed.utils.misc import sub_dict
+
+
+def get_num_task(dataset):
+ dataset = dataset.lower()
+ """ used in molecule_finetune.py """
+ if dataset == 'tox21':
+ return 12
+ elif dataset in ['hiv', 'bace', 'bbbp', 'donor', "bbb_martins", "caco2_wang", "cyp2c9_veith", "half_life_obach", "ld50_zhu"]:
+ return 1
+ elif dataset == 'pcba':
+ return 92
+ elif dataset == 'muv':
+ return 17
+ elif dataset == 'toxcast':
+ return 617
+ elif dataset == 'sider':
+ return 27
+ elif dataset == 'clintox':
+ return 2
+ raise ValueError('Invalid dataset name.')
+
+
+class MoleculePropertyPredictionModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+ self.num_tasks = get_num_task(model_cfg.dataset_name)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["molecule_property_prediction"] = {
+ "forward_fn": self.forward_molecule_property_prediction,
+ "predict_fn": self.predict_molecule_property_prediction,
+ # TODO: 这里label的featurize和collator如何定义呢
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["molecule"]),
+ "label": self.featurizers["classlabel"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["molecule"]),
+ "label": self.collators["classlabel"]
+ })
+ }
+
+ # TODO: Add type hints to the parameters.
+ @abstractmethod
+ def forward_molecule_property_prediction(self,
+ molecule,
+ label
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_molecule_property_prediction(self,
+ molecule
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/molecule_question_answering.py b/open_biomed/models/task_models/molecule_question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3c35dd179e966b0ebaf19905e232dc6848a4127
--- /dev/null
+++ b/open_biomed/models/task_models/molecule_question_answering.py
@@ -0,0 +1,45 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import EnsembleFeaturizer, Featurized
+from open_biomed.utils.misc import sub_dict
+
+class MoleculeQAModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["molecule_question_answering"] = {
+ "forward_fn": self.forward_molecule_question_answering,
+ "predict_fn": self.predict_molecule_question_answering,
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["molecule", "text"]),
+ "label": self.featurizers["text"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["molecule", "text"]),
+ "label": self.collators["text"]
+ })
+ }
+
+ @abstractmethod
+ def forward_molecule_question_answering(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_molecule_question_answering(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ ) -> List[Text]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/mutation_text_translation.py b/open_biomed/models/task_models/mutation_text_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..44cf9f4520161faa7d09a82541018be401690f32
--- /dev/null
+++ b/open_biomed/models/task_models/mutation_text_translation.py
@@ -0,0 +1,81 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+
+from open_biomed.data import Protein, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurizer, Featurized
+
+class MutationExplanationModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ featurizer, collator = self.featurizer_mutation_explanation()
+ self.supported_tasks["mutation_explanation"] = {
+ "forward_fn": self.forward_mutation_explanation,
+ "predict_fn": self.predict_mutation_explanation,
+ "featurizer": featurizer,
+ "collator": collator,
+ }
+
+ @abstractmethod
+ def forward_mutation_explanation(self,
+ wild_type: Featurized[Protein],
+ mutant: Featurized[Any],
+ label: Featurized[Text],
+ **kwargs,
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_mutation_explanation(self,
+ wild_type: Featurized[Protein],
+ mutant: Featurized[Any],
+ **kwargs,
+ ) -> List[Text]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def featurizer_mutation_explanation(self) -> Tuple[Featurizer, Collator]:
+ raise NotImplementedError
+
+class MutationEngineeringModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ featurizer, collator = self.featurizer_mutation_engineering()
+ self.supported_tasks["mutation_engineering"] = {
+ "forward_fn": self.forward_mutation_engineering,
+ "predict_fn": self.predict_mutation_engineering,
+ "featurizer": featurizer,
+ "collator": collator,
+ }
+
+ @abstractmethod
+ def forward_mutation_engineering(self,
+ wild_type: Featurized[Protein],
+ prompt: Featurized[Text],
+ label: Featurized[Any],
+ **kwargs,
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_mutation_engineering(self,
+ wild_type: Featurized[Protein],
+ prompt: Featurized[Text],
+ position: Optional[torch.LongTensor]=None,
+ **kwargs,
+ ) -> List[List[Tuple[Protein, str]]]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def featurizer_mutation_engineering(self) -> Tuple[Featurizer, Collator]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/protein_folding.py b/open_biomed/models/task_models/protein_folding.py
new file mode 100644
index 0000000000000000000000000000000000000000..43b4bfb971b465a67777e495e30be65fde04e9f5
--- /dev/null
+++ b/open_biomed/models/task_models/protein_folding.py
@@ -0,0 +1,42 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union, Tuple
+
+import torch
+
+from open_biomed.data import Protein
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import Collator, EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurizer, EnsembleFeaturizer, Featurized
+from open_biomed.utils.misc import sub_dict
+
+
+class ProteinFoldingModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ featurizer, collator = self.featurizer_protein_folding()
+ self.supported_tasks["protein_folding"] = {
+ "forward_fn": self.forward_protein_folding,
+ "predict_fn": self.predict_protein_folding,
+ "featurizer": featurizer,
+ "collator": collator,
+ }
+
+ @abstractmethod
+ def forward_protein_folding(self,
+ protein: Featurized[Protein],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_protein_folding(self,
+ protein: Featurized[Protein],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def featurizer_protein_folding(self) -> Tuple[Featurizer, Collator]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/protein_molecule_docking.py b/open_biomed/models/task_models/protein_molecule_docking.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd9aa56bea650fa299002b2f3c877479a4aabbb3
--- /dev/null
+++ b/open_biomed/models/task_models/protein_molecule_docking.py
@@ -0,0 +1,50 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Molecule, Pocket
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import EnsembleFeaturizer, Featurized
+from open_biomed.utils.misc import sub_dict
+
+class PocketMolDockModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["pocket_molecule_docking"] = {
+ "forward_fn": self.forward_pocket_molecule_docking,
+ "predict_fn": self.predict_pocket_molecule_docking,
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["molecule", "pocket"]),
+ "label": self.featurizers["molecule"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["molecule", "pocket"]),
+ "label": self.collators["molecule"]
+ })
+ }
+
+ @abstractmethod
+ def forward_pocket_molecule_docking(self,
+ molecule: Featurized[Molecule],
+ pocket: Featurized[Pocket],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_pocket_molecule_docking(self,
+ molecule: Featurized[Molecule],
+ pocket: Featurized[Pocket],
+ ) -> List[Molecule]:
+ raise NotImplementedError
+
+#TODO: implement blind docking
+class BlindMolDockModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
\ No newline at end of file
diff --git a/open_biomed/models/task_models/protein_question_answering.py b/open_biomed/models/task_models/protein_question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bf7f34b42b9f6ad92808f6f2e6a1904fefabd42
--- /dev/null
+++ b/open_biomed/models/task_models/protein_question_answering.py
@@ -0,0 +1,45 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Protein, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurized, EnsembleFeaturizer
+from open_biomed.utils.misc import sub_dict
+
+class ProteinQAModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["protein_question_answering"] = {
+ "forward_fn": self.forward_protein_question_answering,
+ "predict_fn": self.predict_protein_question_answering,
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["protein", "text"]),
+ "label": self.featurizers["text"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["protein", "text"]),
+ "label": self.collators["text"]
+ })
+ }
+
+ @abstractmethod
+ def forward_protein_question_answering(self,
+ protein: Featurized[Protein],
+ text: Featurized[Text],
+ label: Featurized[Text],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_protein_question_answering(self,
+ protein: Featurized[Protein],
+ text: Featurized[Text],
+ ) -> List[Text]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/protein_text_translation.py b/open_biomed/models/task_models/protein_text_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..73081fdf252d5d2f012a9db910dddadaf83ab650
--- /dev/null
+++ b/open_biomed/models/task_models/protein_text_translation.py
@@ -0,0 +1,96 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from open_biomed.data import Protein, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.models.functional_model_registry import TEXT_ENCODER_REGISTRY, PROTEIN_DECODER_REGISTRY
+from open_biomed.models.misc import MLP
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import Featurized, EnsembleFeaturizer
+
+class TextBasedProteinGenerationModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["text_based_protein_generation"] = {
+ "forward_fn": self.forward_text_based_protein_generation,
+ "predict_fn": self.predict_text_based_protein_generation,
+ "featurizer": EnsembleFeaturizer({
+ "text": self.featurizers["text"],
+ "label": self.featurizers["protein"],
+ }),
+ "collator": EnsembleCollator({
+ "text": self.collators["text"],
+ "label": self.collators["protein"]
+ })
+ }
+
+ @abstractmethod
+ def forward_text_based_protein_generation(self,
+ text: Featurized[Text],
+ label: Featurized[Protein],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_text_based_protein_generation(self,
+ text: Featurized[Text]
+ ) -> List[Protein]:
+ raise NotImplementedError
+
+class EnsembleTextBasedProteinGenerationModel(TextBasedProteinGenerationModel):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+ self.text_config = Config(config_file=model_cfg.text.config_file).model
+ self.text_model = TEXT_ENCODER_REGISTRY[self.text_config.name](self.text_config)
+ if hasattr(model_cfg.text, "model_checkpoint"):
+ self.text_model.load_from_checkpoint(model_cfg.text.model_checkpoint)
+ for param in self.text_model.parameters():
+ param.requires_grad = False
+ self.protein_config = Config(config_file=model_cfg.protein.config_file).model
+ self.protein_model = PROTEIN_DECODER_REGISTRY[self.protein_config.name](self.protein_config)
+ if hasattr(model_cfg.protein, "model_checkpoint"):
+ self.protein_model.load_from_checkpoint(model_cfg.protein.model_checkpoint)
+ if getattr(model_cfg.protein, "freeze", False):
+ self.protein_model.freeze_parameters()
+ self.connector = MLP(
+ **model_cfg.connector.todict()
+ )
+ self.dropout = nn.Dropout(0.2)
+
+ text_featurizer, text_collator = self.text_model.get_text_processor()
+ protein_featurizer, protein_collator = self.protein_model.get_protein_processor()
+ self.featurizers = {
+ "text": text_featurizer,
+ "protein": protein_featurizer,
+ }
+ self.collators = {
+ "text": text_collator,
+ "protein": protein_collator,
+ }
+
+ self._add_task()
+
+ def forward_text_based_protein_generation(self,
+ text: Featurized[Text],
+ label: Featurized[Protein]
+ ) -> Dict[str, torch.Tensor]:
+ text_embeds = self.text_model.encode_text(text)
+ text_embeds = self.dropout(self.connector(text_embeds))
+ return self.protein_model.generate_loss(label, add_embeds=text_embeds[:, 0, :].unsqueeze(1), add_attention_mask=text.attention_mask[:, 0].unsqueeze(1))
+ # return self.protein_model.generate_loss(label, add_embeds=text_embeds, add_attention_mask=text.attention_mask)
+
+ def predict_text_based_protein_generation(self,
+ text: Featurized[Text]
+ ) -> List[Protein]:
+ text_embeds = self.text_model.encode_text(text)
+ text_embeds = self.dropout(self.connector(text_embeds))
+ return self.protein_model.generate_protein(add_embeds=text_embeds[:, 0, :].unsqueeze(1), add_attention_mask=text.attention_mask[:, 0].unsqueeze(1))
+ # return self.protein_model.generate_protein(add_embeds=text_embeds, add_attention_mask=text.attention_mask)
\ No newline at end of file
diff --git a/open_biomed/models/task_models/structure_based_drug_design.py b/open_biomed/models/task_models/structure_based_drug_design.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2f0341ba7d14e3b62607ff91cab06dafe82a9f5
--- /dev/null
+++ b/open_biomed/models/task_models/structure_based_drug_design.py
@@ -0,0 +1,43 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Molecule, Pocket
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import EnsembleFeaturizer, Featurized
+from open_biomed.utils.misc import sub_dict
+
+class StructureBasedDrugDesignModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["structure_based_drug_design"] = {
+ "forward_fn": self.forward_structure_based_drug_design,
+ "predict_fn": self.predict_structure_based_drug_design,
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["pocket"]),
+ "label": self.featurizers["molecule"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["pocket"]),
+ "label": self.collators["molecule"]
+ })
+ }
+
+ @abstractmethod
+ def forward_structure_based_drug_design(self,
+ pocket: Featurized[Pocket],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_structure_based_drug_design(self,
+ pocket: Featurized[Pocket],
+ ) -> List[Molecule]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/text_based_molecule_editing.py b/open_biomed/models/task_models/text_based_molecule_editing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec53e64f2b0d82da9fdd8514dfa423285d32941e
--- /dev/null
+++ b/open_biomed/models/task_models/text_based_molecule_editing.py
@@ -0,0 +1,45 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import EnsembleFeaturizer, Featurized
+from open_biomed.utils.misc import sub_dict
+
+class TextBasedMoleculeEditingModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["text_based_molecule_editing"] = {
+ "forward_fn": self.forward_text_based_molecule_editing,
+ "predict_fn": self.predict_text_based_molecule_editing,
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["molecule", "text"]),
+ "label": self.featurizers["molecule"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["molecule", "text"]),
+ "label": self.collators["molecule"]
+ })
+ }
+
+ @abstractmethod
+ def forward_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ label: Featurized[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ @torch.no_grad()
+ def predict_text_based_molecule_editing(self,
+ molecule: Featurized[Molecule],
+ text: Featurized[Text],
+ ) -> List[Molecule]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/task_models/text_guided_molecule_generation.py b/open_biomed/models/task_models/text_guided_molecule_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..868f75cbbb1a15c49b8660966e92171e22c5628e
--- /dev/null
+++ b/open_biomed/models/task_models/text_guided_molecule_generation.py
@@ -0,0 +1,42 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
+
+import torch
+
+from open_biomed.data import Molecule, Text
+from open_biomed.models.base_model import BaseModel
+from open_biomed.utils.collator import EnsembleCollator
+from open_biomed.utils.config import Config
+from open_biomed.utils.featurizer import EnsembleFeaturizer
+from open_biomed.utils.misc import sub_dict
+
+class TextGuidedMoleculeGenerationModel(BaseModel, ABC):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+
+ def _add_task(self) -> None:
+ self.supported_tasks["text_guided_molecule_generation"] = {
+ "forward_fn": self.forward_text_guided_molecule_generation,
+ "predict_fn": self.predict_text_guided_molecule_generation,
+ "featurizer": EnsembleFeaturizer({
+ **sub_dict(self.featurizers, ["text"]),
+ "label": self.featurizers["molecule"]
+ }),
+ "collator": EnsembleCollator({
+ **sub_dict(self.collators, ["text"]),
+ "label": self.collators["molecule"]
+ })
+ }
+
+ @abstractmethod
+ def forward_text_guided_molecule_generation(self,
+ text: List[Text],
+ label: List[Molecule],
+ ) -> Dict[str, torch.Tensor]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def predict_text_guided_molecule_generation(self,
+ text: List[Text],
+ ) -> List[Molecule]:
+ raise NotImplementedError
\ No newline at end of file
diff --git a/open_biomed/models/text/pretrained_lm.py b/open_biomed/models/text/pretrained_lm.py
new file mode 100644
index 0000000000000000000000000000000000000000..4eeb3d7d1f322351a74a952de2aa49a074bc810d
--- /dev/null
+++ b/open_biomed/models/text/pretrained_lm.py
@@ -0,0 +1,27 @@
+from typing import List, Tuple, Union
+
+import torch
+from transformers import AutoModel, AutoTokenizer, DataCollatorWithPadding
+
+from open_biomed.data import Text
+from open_biomed.models.functional_model import TextEncoder
+from open_biomed.utils.config import Config
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.featurizer import Featurized, TextFeaturizer, TextTransformersFeaturizer
+
+class PretrainedLMForTextEncoding(TextEncoder):
+ def __init__(self, model_cfg: Config) -> None:
+ super().__init__(model_cfg)
+ self.model = AutoModel.from_pretrained(model_cfg.model_name_or_path)
+ self.tokenizer = AutoTokenizer.from_pretrained(model_cfg.model_name_or_path)
+ self.featurizer = TextTransformersFeaturizer(self.tokenizer, model_cfg.max_length)
+ self.collator = DataCollatorWithPadding(self.tokenizer, padding=True)
+
+ def get_text_processor(self) -> Tuple[TextFeaturizer, Collator]:
+ return self.featurizer, self.collator
+
+ def encode_text(self, text: Union[List[Text], Featurized[Text]]) -> torch.Tensor:
+ if isinstance(text, list):
+ text = self.collator([self.featurizer(t) for t in text]).to(self.model.device)
+ return self.model(**text).last_hidden_state
+
\ No newline at end of file
diff --git a/open_biomed/scripts/chat.py b/open_biomed/scripts/chat.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa29d908c758b8c331494a15c02f85ec7a80985e
--- /dev/null
+++ b/open_biomed/scripts/chat.py
@@ -0,0 +1,39 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+import argparse
+import logging
+
+from open_biomed.data import Molecule, Protein, Text
+from open_biomed.models.foundation_models.biomedgpt import BioMedGPT4Chat
+
+def chat_biomedgpt():
+ agent = BioMedGPT4Chat.from_pretrained("./checkpoints/biomedgpt-10b/", "cuda:4")
+ molecule = Molecule.from_smiles("CC(=CCC1=CC(=CC(=C1O)CC=C(C)C)/C=C/C(=O)C2=C(C=C(C=C2)O)O)C")
+ protein = Protein.from_fasta("MAKEDTLEFPGVVKELLPNATFRVELDNGHELIAVMAGKMRKNRIRVLAGDKVQVEMTPYDLSKGRINYRFK")
+ agent.append_molecule(molecule)
+ print(agent.chat(Text.from_str("Please describe this molecule.")))
+ agent.reset()
+ agent.append_protein(protein)
+ print(agent.chat(Text.from_str("What is the function of this protein?")))
+
+CHAT_UNIT_TESTS = {
+ "biomedgpt": chat_biomedgpt,
+}
+
+if __name__ == "__main__":
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+ )
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--model", type=str, default="biomedgpt")
+ args = parser.parse_args()
+
+ if args.model not in CHAT_UNIT_TESTS:
+ raise NotImplementedError(f"{args.model} is not currently supported for chat!")
+ else:
+ CHAT_UNIT_TESTS[args.model]()
\ No newline at end of file
diff --git a/open_biomed/scripts/inference.py b/open_biomed/scripts/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..a251cf734af3dd7b7994a00a15c0c002db0d7936
--- /dev/null
+++ b/open_biomed/scripts/inference.py
@@ -0,0 +1,250 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+import torch
+torch.set_printoptions(precision=4, sci_mode=False)
+
+import argparse
+
+from open_biomed.core.pipeline import InferencePipeline, EnsemblePipeline
+from open_biomed.core.tool_misc import MutationToSequence
+from open_biomed.data import Molecule, Text, Protein, Pocket
+from open_biomed.tasks.aidd_tasks.protein_molecule_docking import VinaDockTask
+
+os.environ["dataset_name"] = "BBBP"
+
+def test_text_based_molecule_editing():
+ pipeline = InferencePipeline(
+ task="text_based_molecule_editing",
+ model="molt5",
+ model_ckpt="./checkpoints/server/text_based_molecule_editing_biot5.ckpt",
+ device="cuda:0"
+ )
+ input_smiles = "Nc1[nH]c(C(=O)c2ccccc2)c(-c2ccccn2)c1C(=O)c1c[nH]c2ccc(Br)cc12"
+ input_text = "This molecule can bind with recombinant human 15-LOX-1"
+ outputs = pipeline.run(
+ molecule=Molecule.from_smiles(input_smiles),
+ text=Text.from_str(input_text),
+ )
+ print(f"Input SMILES: {input_smiles}")
+ print(f"Input Text: {input_text}")
+ print(outputs[0])
+ return pipeline
+
+def test_pocket_molecule_docking():
+ pipeline = InferencePipeline(
+ task="pocket_molecule_docking",
+ model="pharmolix_fm",
+ model_ckpt="./checkpoints/server/pharmolix_fm.ckpt",
+ device="cuda:0"
+ )
+ protein = Protein.from_pdb_file("./checkpoints/server/test_data/4xli_B.pdb")
+ ligand = Molecule.from_sdf_file("./checkpoints/server/test_data/4xli_B_ref.sdf")
+ pocket = Pocket.from_protein_ref_ligand(protein, ligand)
+ outputs = pipeline.run(
+ molecule=ligand,
+ pocket=pocket,
+ )
+ print(outputs)
+ return pipeline
+
+def test_structure_based_drug_design():
+ pipeline = InferencePipeline(
+ task="structure_based_drug_design",
+ model="pharmolix_fm",
+ model_ckpt="./checkpoints/server/pharmolix_fm.ckpt",
+ # model="molcraft",
+ # model_ckpt="./checkpoints/molcraft/last_updated.ckpt",
+ device="cuda:0"
+ )
+ protein = Protein.from_pdb_file("./checkpoints/server/test_data/4xli_B.pdb")
+ ligand = Molecule.from_sdf_file("./checkpoints/server/test_data/4xli_B_ref.sdf")
+ from pytorch_lightning import seed_everything
+ seed_everything(1234)
+ pocket = Pocket.from_protein_ref_ligand(protein, ligand)
+ pocket.estimated_num_atoms = ligand.get_num_atoms()
+ for i in range(1):
+ outputs = pipeline.run(
+ pocket=pocket
+ )
+ print(outputs[0][0])
+ print(outputs[0][0].conformer)
+ from open_biomed.tasks.aidd_tasks.structure_based_drug_design import calc_vina_molecule_metrics
+ print(calc_vina_molecule_metrics(outputs[0][0], protein))
+ print(outputs)
+ return pipeline
+
+def test_protein_molecule_docking():
+ protein = Protein.from_pdb_file("./checkpoints/server/test_data/4xli_B.pdb")
+ ligand = Molecule.from_sdf_file("./checkpoints/server/test_data/4xli_B_ref.sdf")
+ vina = VinaDockTask(mode="dock")
+ print(vina.run(ligand, protein)[0][0])
+ return vina
+
+def test_molecule_question_answering():
+ pipeline = InferencePipeline(
+ task="molecule_question_answering",
+ model="biot5",
+ model_ckpt="./checkpoints/server/molecule_question_answering_biot5.ckpt",
+ device="cuda:0"
+ )
+ molecule = Molecule.from_smiles("C[C@@H]1CCCCO[C@@H](CN(C)C(=O)Cc2ccccc2)[C@@H](C)CN([C@@H](C)CO)C(=O)c2cc(NS(C)(=O)=O)ccc2O1")
+ question = Text.from_str("Could you provide the systematic name of this compound according to IUPAC nomenclature?")
+ outputs = pipeline.run(
+ molecule=molecule,
+ text=question,
+ )
+ print(outputs)
+ return pipeline
+
+def test_protein_question_answering():
+ pipeline = InferencePipeline(
+ task="protein_question_answering",
+ model="biot5",
+ model_ckpt="./checkpoints/server/protein_question_answering_biot5.ckpt",
+ device="cuda:0"
+ )
+ protein = Protein.from_fasta("MRVGVIRFPGSNCDRDVHHVLELAGAEPEYVWWNQRNLDHLDAVVIPGGFSYGDYLRAGAIAAITPVMDAVRELVREEKPVLGICNGAQILAEVGLVPGVFTVNEHPKFNCQWTELRVKTTRTPFTGLFKKDEVIRMPVAHAEGRYYHDNISEVWENDQVVLQFHGENPNGSLDGITGVCDESGLVCAVMPHPERASEEILGSVDGFKFFRGILKFRG")
+ question = Text.from_str("Inspect the protein sequence and offer a concise description of its properties.")
+ outputs = pipeline.run(
+ protein=protein,
+ text=question,
+ )
+ print(outputs)
+ return pipeline
+
+def test_mutation_explanation():
+ pipeline = InferencePipeline(
+ task="mutation_explanation",
+ model="mutaplm",
+ model_ckpt="./checkpoints/server/mutaplm.pth",
+ device="cuda:0"
+ )
+ protein = Protein.from_fasta("MTLENVLEAARHLHQTLPALSEFGNWPTDLTATGLQPRAIPATPLVQALDQPGSPRTTGLVQAIRSAAHLAHWKRTYTEAEVGADFRNRYGYFELFGPTGHFHSTQLRGYVAYWGAGLDYDWHSHQAEELYLTLAGGAVFKVDGERAFVGAEGTRLHASWQSAAMSTGDQPILTFVLWRGEGLNALPRMDAA")
+ mutation = "H163A"
+ outputs = pipeline.run(
+ protein=protein,
+ mutation=mutation,
+ )
+ print(outputs)
+ return pipeline
+
+def test_mutation_engineering():
+ pipeline = InferencePipeline(
+ task="mutation_engineering",
+ model="mutaplm",
+ model_ckpt="./checkpoints/server/mutaplm.pth",
+ device="cuda:1"
+ )
+ protein = Protein.from_fasta("MASDAAAEPSSGVTHPPRYVIGYALAPKKQQSFIQPSLVAQAASRGMDLVPVDASQPLAEQGPFHLLIHALYGDDWRAQLVAFAARHPAVPIVDPPHAIDRLHNRISMLQVVSELDHAADQDSTFGIPSQVVVYDAAALADFGLLAALRFPLIAKPLVADGTAKSHKMSLVYHREGLGKLRPPLVLQEFVNHGGVIFKVYVVGGHVTCVKRRSLPDVSPEDDASAQGSVSFSQVSNLPTERTAEEYYGEKSLEDAVVPPAAFINQIAGGLRRALGLQLFNFDMIRDVRAGDRYLVIDINYFPGYAKMPGYETVLTDFFWEMVHKDGVGNQQEEKGANHVVVK")
+ prompt = Text.from_str("Strongly enhanced InsP6 kinase activity. The mutation in the ITPK protein causes a change in its catalytic activity.")
+ outputs = pipeline.run(
+ protein=protein,
+ text=prompt
+ )
+ print(outputs[0])
+ converter = MutationToSequence()
+ outputs = converter.run([protein for i in range(len(outputs[0][0]))], outputs[0][0])
+ print(outputs[0][0], outputs[1][0])
+ return pipeline
+
+def test_protein_generation():
+ from open_biomed.models.functional_model_registry import PROTEIN_DECODER_REGISTRY
+ from open_biomed.utils.config import Config
+ config = Config(config_file="./configs/model/progen.yaml").model
+ model = PROTEIN_DECODER_REGISTRY["progen"](config)
+ model = model.to("cuda:0")
+ print(model.generate_protein()[0])
+ protein = Protein.from_fasta('GFLPFRGADEGLAAREAATLAARGTAARAYREDSWAVPVPRGLLGDLTARVAALGAASPPPADPLAVTLDLHHVTAEVALTTVLDAATLVHGQTRVLSAEDAAEAATAAAAATEAYLERLQDFVLFMSASVRVWRRGNAAGATGPEWDQWYTVADRDALGSAPTHLAVLGRQADALCHFVLDRVAWGTCGTPLWSGDEDLGNVVATFAGYADRLATAPRDLIM')
+ protein = model.collator([model.featurizer(protein)])
+ protein = {
+ "input_ids": protein["input_ids"].to("cuda:0"),
+ "attention_mask": protein["attention_mask"].to("cuda:0"),
+ }
+ print(model.generate_loss(protein))
+
+
+# TODO: support other datasets
+def test_molecule_property_prediction():
+ OUTPUT_PROMPTS = {
+ "BBBP": "The blood-brain barrier penetration of the molecule is {output}",
+ "SIDER": "The possibility of the molecule to exhibit the side effects are:\n Hepatobiliary disorders: {output[0]:.4f}\n Metabolism and nutrition disorders: {output[1]:.4f}\nProduct issues: {output[2]:.4f}\nEye disorders: {output[3]:.4f}\nInvestigations: {output[4]:.4f}\nMusculoskeletal and connective tissue disorders: {output[5]:.4f}\nGastrointestinal disorders :{output[6]:.4f}\nSocial circumstances: {output[7]:.4f}\nImmune system disorders: {output[8]:.4f}\nReproductive system and breast disorders: {output[9]:.4f}\nNeoplasms benign, malignant and unspecified (incl cysts and polyps): {output[10]:.4f}\nGeneral disorders and administration site conditions: {output[11]:.4f}\nEndocrine disorders: {output[12]:.4f}\nSurgical and medical procedures: {output[13]:.4f}\nVascular disorders: {output[14]:.4f}\nBlood and lymphatic system disorders: {output[15]:.4f}\nSkin and subcutaneous tissue disorders: {output[16]:.4f}\nCongenital, familial and genetic disorders: {output[17]:.4f}\nInfections and infestations: {output[18]:.4f}\nRespiratory, thoracic and mediastinal disorders: {output[19]:.4f}\nPsychiatric disorders: {output[20]:.4f}\nRenal and urinary disorders: {output[21]:.4f}\nPregnancy, puerperium and perinatal conditions: {output[22]:.4f}\nEar and labyrinth disorders: {output[23]:.4f}\nCardiac disorders: {output[24]:.4f}\nNervous system disorders: {output[25]:.4f}\nInjury, poisoning and procedural complications: {output[26]:.4f}\n",
+ }
+ OUTPUT_PROMPTS_REGRESSION = {
+ "caco2_wang": "The rate of drug passing through the Caco-2 cells is {output}",
+ "half_life_obach": "The half-life of the molecule is {output}",
+ "ld50_zhu": "The most conservative dose of the molecule that can lead to lethal adverse effects is {output}",
+ }
+ pipelines = {}
+ for task in OUTPUT_PROMPTS:
+ pipelines[task] = InferencePipeline(
+ task="molecule_property_prediction",
+ model="graphmvp",
+ model_ckpt=f"./checkpoints/server/graphmvp-{task}.ckpt",
+ additional_config=f"./configs/dataset/{task.lower()}.yaml",
+ device="cuda:0",
+ output_prompt=OUTPUT_PROMPTS[task],
+ )
+ for task in OUTPUT_PROMPTS_REGRESSION:
+ pipelines[task] = InferencePipeline(
+ task="molecule_property_prediction_regression",
+ model="graphmvp_regression",
+ model_ckpt=f"./checkpoints/server/graphmvp-{task}.ckpt",
+ additional_config=f"./configs/dataset/{task.lower()}.yaml",
+ device="cuda:0",
+ output_prompt=OUTPUT_PROMPTS_REGRESSION[task],
+ )
+ pipeline = EnsemblePipeline(pipelines)
+ input_smiles = "Nc1[nH]c(C(=O)c2ccccc2)c(-c2ccccn2)c1C(=O)c1c[nH]c2ccc(Br)cc12"
+ for task in OUTPUT_PROMPTS:
+ outputs = pipeline.run(
+ molecule=Molecule.from_smiles(input_smiles),
+ task=task,
+ )
+ print(outputs)
+ for task in OUTPUT_PROMPTS_REGRESSION:
+ outputs = pipeline.run(
+ molecule=Molecule.from_smiles(input_smiles),
+ task=task,
+ )
+ print(outputs)
+ return pipeline
+
+
+def test_protein_folding():
+ pipeline = InferencePipeline(
+ task="protein_folding",
+ model="esmfold",
+ model_ckpt="./checkpoints/server/esmfold.ckpt",
+ device="cuda:1"
+ )
+ protein = Protein.from_fasta("MASDAAAEPSSGVTHPPRYVIGYALAPKKQQSFIQPSLVAQAASRGMDLVPVDASQPLAEQGPFHLLIHALYGDDWRAQLVAFAARHPAVPIVDPPHAIDRLHNRISMLQVVSELDHAADQDSTFGIPSQVVVYDAAALADFGLLAALRFPLIAKPLVADGTAKSHKMSLVYHREGLGKLRPPLVLQEFVNHGGVIFKVYVVGGHVTCVKRRSLPDVSPEDDASAQGSVSFSQVSNLPTERTAEEYYGEKSLEDAVVPPAAFINQIAGGLRRALGLQLFNFDMIRDVRAGDRYLVIDINYFPGYAKMPGYETVLTDFFWEMVHKDGVGNQQEEKGANHVVVK")
+ outputs = pipeline.run(
+ protein=protein,
+ )
+ print(outputs[0][0], outputs[1][0])
+ return pipeline
+
+INFERENCE_UNIT_TESTS = {
+ "text_based_molecule_editing": test_text_based_molecule_editing,
+ "pocket_molecule_docking": test_pocket_molecule_docking,
+ "structure_based_drug_design": test_structure_based_drug_design,
+ "molecule_question_answering": test_molecule_question_answering,
+ "protein_question_answering": test_protein_question_answering,
+ "mutation_explanation": test_mutation_explanation,
+ "mutation_engineering": test_mutation_engineering,
+ "protein_generation": test_protein_generation,
+ "molecule_property_prediction": test_molecule_property_prediction,
+ "protein_folding": test_protein_folding,
+}
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--task", type=str, default="protein_folding")
+ args = parser.parse_args()
+
+ if args.task not in INFERENCE_UNIT_TESTS:
+ raise NotImplementedError(f"{args.task} is not currently supported!")
+ else:
+ INFERENCE_UNIT_TESTS[args.task]()
+
\ No newline at end of file
diff --git a/open_biomed/scripts/run_server.py b/open_biomed/scripts/run_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3eb345a4c89b4f4fedc3085fe483f413a6431e9
--- /dev/null
+++ b/open_biomed/scripts/run_server.py
@@ -0,0 +1,631 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import torch.nn.functional as F
+import uvicorn
+import random
+import copy
+import asyncio
+import logging
+import subprocess
+from typing import Optional, List, Dict, Callable, Any, Literal
+
+# import function
+from open_biomed.data import Molecule, Text, Protein, Pocket
+from open_biomed.core.tool_misc import MutationToSequence
+from open_biomed.core.oss_warpper import oss_warpper
+from open_biomed.core.tool_registry import TOOLS
+
+
+app = FastAPI()
+
+
+class IO_Reader:
+ def __init__(self):
+ pass
+ @staticmethod
+ def get_molecule(string):
+ if string.endswith(".sdf"):
+ return Molecule.from_sdf_file(string)
+ elif string.endswith(".pkl"):
+ return Molecule.from_binary_file(string)
+ else:
+ return Molecule.from_smiles(string)
+
+ @staticmethod
+ def get_protein(string):
+ if string.endswith(".pdb"):
+ return Protein.from_pdb_file(string)
+ elif string.endswith(".pkl"):
+ return Protein.from_binary_file(string)
+ else:
+ return Protein.from_fasta(string)
+
+ @staticmethod
+ def get_pocket(string):
+ return Pocket.from_binary_file(string)
+
+ @staticmethod
+ def get_text(string):
+ return Text.from_str(string)
+
+
+
+
+molecule_property_prediction_prompt = {
+ "BBBP": "The blood-brain barrier prediction result is {output}. "
+ "This result indicates the model's prediction of whether the compound can effectively penetrate the blood-brain barrier. "
+ "A positive result suggests that the compound may have potential for central nervous system targeting, "
+ "while a negative result implies limited permeability.",
+
+ "ClinTox": "The clinical toxicity prediction result is {output}. "
+ "This result reflects the model's assessment of the likelihood that the compound will fail clinical trials due to toxicity concerns. "
+ "A positive result indicates a higher risk of toxicity, while a negative result suggests the compound is less likely to exhibit significant toxicity in clinical settings.",
+
+ "Tox21": "The Tox21 toxicity assessment result is {output}. "
+ "This result provides an evaluation of the compound's potential toxicity, focusing on nuclear receptors and stress response pathways. "
+ "A positive result indicates the presence of toxic effects, while a negative result suggests the compound is less likely to exhibit these toxicities.",
+
+ "ToxCast": "The ToxCast toxicity screening result is {output}. "
+ "This result is based on high-throughput in vitro screening and indicates the compound's potential toxicity profile. "
+ "A positive result suggests significant toxicity, while a negative result implies lower toxicity risk.",
+
+ "SIDER": "The SIDER adverse drug reaction analysis result is {output}. "
+ "This result provides insights into the potential adverse drug reactions (ADRs) associated with the compound. "
+ "A positive result indicates a higher likelihood of adverse reactions, while a negative result suggests fewer potential ADRs.",
+
+ "HIV": "The HIV inhibition prediction result is {output}. "
+ "This result indicates the model's prediction of the compound's ability to inhibit HIV replication. "
+ "A positive result suggests strong inhibitory activity, while a negative result implies limited effectiveness against HIV.",
+
+ "BACE": "The BACE-1 activity prediction result is {output}. "
+ "This result provides a prediction of the compound's binding affinity to human β-secretase 1 (BACE-1). "
+ "A positive result indicates strong binding activity, suggesting potential as a BACE-1 inhibitor, while a negative result implies weaker binding.",
+
+ "MUV": "The MUV virtual screening validation result is {output}. "
+ "This result indicates the model's assessment of the compound's potential as a hit in virtual screening. "
+ "A positive result suggests the compound is likely to be active against the target, while a negative result implies lower activity."
+}
+
+
+# Define the request body model
+class TaskRequest(BaseModel):
+ task: str
+ model: Optional[str] = None
+ config: Optional[str] = None
+ visualize: Optional[str] = None
+ molecule: Optional[str] = None
+ protein: Optional[str] = None
+ pocket: Optional[str] = None
+ text: Optional[str] = None
+ dataset: Optional[str] = None
+ query: Optional[str] = None
+ mutation: Optional[str] = None
+ indices: Optional[str] = None
+ property: Optional[Literal["QED", "SA", "LogP", "Lipinski"]] = None
+ molecule_1: Optional[str] = None
+ molecule_2: Optional[str] = None
+ similarity: Optional[float] = None
+ value: Optional[str] = None
+
+
+class SearchRequest(BaseModel):
+ task: str
+ query: Optional[str] = None
+ molecule: Optional[str] = None
+ threshold: Optional[str] = None
+
+
+class TaskConfig:
+ def __init__(self, task_name: str, required_inputs: List[str], pipeline_key: str, handler_function: Callable, is_async: bool = False):
+ self.task_name = task_name
+ self.required_inputs = required_inputs
+ self.pipeline_key = pipeline_key
+ self.handler_function = handler_function
+ self.is_async = is_async
+
+ def validate_inputs(self, request: Dict[str, Any]):
+ missing_inputs = [key for key in self.required_inputs if key not in request]
+ if missing_inputs:
+ raise HTTPException(status_code=400, detail=f"Missing required inputs: {', '.join(missing_inputs)}")
+
+
+
+class TaskLoader:
+ def __init__(self):
+ self.tasks = {}
+
+ def register_task(self, task_config: TaskConfig):
+ self.tasks[task_config.task_name] = task_config
+
+ def get_task(self, task_name: str):
+ task = self.tasks.get(task_name)
+ if not task:
+ raise HTTPException(status_code=400, detail="Invalid task_name")
+ return task
+
+
+
+# Handlers for run_pipeline
+def handle_text_based_molecule_editing(request: TaskRequest, pipeline):
+ required_inputs = ["molecule", "text"]
+ molecule = IO_Reader.get_molecule(request.molecule)
+ text = IO_Reader.get_text(request.text)
+ outputs = pipeline.run(molecule=molecule, text=text)
+ smiles = outputs[0][0].smiles
+ path = outputs[1][0]
+ return {"task": request.task, "model": request.model, "molecule": path, "molecule_preview": smiles}
+
+def handle_structure_based_drug_design(request: TaskRequest, pipeline):
+ required_inputs = ["pocket"]
+ pocket = Pocket.from_binary_file(request.pocket)
+ outputs = pipeline.run(pocket=pocket)
+ smiles = outputs[0][0].smiles
+ path = outputs[1][0]
+ return {"task": request.task, "model": request.model, "molecule": path, "molecule_preview": smiles}
+
+def handle_molecule_question_answering(request: TaskRequest, pipeline):
+ required_inputs = ["text", "molecule"]
+ text = IO_Reader.get_text(request.text)
+ molecule = IO_Reader.get_molecule(request.molecule)
+ outputs = pipeline.run(molecule=molecule, text=text)
+ text = outputs[0][0].str
+ return {"task": request.task, "model": request.model, "text": text}
+
+def handle_protein_question_answering(request: TaskRequest, pipeline):
+ required_inputs = ["text", "protein"]
+ text = IO_Reader.get_text(request.text)
+ protein = IO_Reader.get_protein(request.protein)
+ outputs = pipeline.run(protein=protein, text=text)
+ text = outputs[0][0].str
+ return {"task": request.task, "model": request.model, "text": text}
+
+def handle_visualize_molecule(request: TaskRequest, pipeline):
+ required_inputs = ["molecule"]
+ #ligand = Molecule.from_binary_file(request.molecule)
+ #outputs = pipeline.run(ligand, config="ball_and_stick", rotate=False)
+ vis_process = [
+ "python3", "./open_biomed/core/visualize.py",
+ "--task", "visualize_molecule",
+ "--molecule_config", request.visualize,
+ "--save_output_filename", "./tmp/molecule_visualization_file.txt",
+ "--molecule", request.molecule]
+ subprocess.Popen(vis_process).communicate()
+ outputs = open("./tmp/molecule_visualization_file.txt", "r").read()
+ oss_file_path = oss_warpper.generate_file_name(outputs)
+ outputs = oss_warpper.upload(oss_file_path, outputs)
+ return {"task": request.task, "image": outputs}
+
+def handle_visualize_complex(request: TaskRequest, pipeline):
+ required_inputs = ["protein", "molecule"]
+ #ligand = Molecule.from_binary_file(request.molecule)
+ #protein = Protein.from_pdb_file(request.protein)
+ #outputs = pipeline.run(molecule=ligand, protein=protein, rotate=True)
+ vis_process = [
+ "python3", "./open_biomed/core/visualize.py",
+ "--task", "visualize_complex",
+ "--save_output_filename", "./tmp/complex_visualization_file.txt",
+ "--molecule", request.molecule,
+ "--protein", request.protein]
+ subprocess.Popen(vis_process).communicate()
+ outputs = open("./tmp/complex_visualization_file.txt", "r").read()
+ oss_file_path = oss_warpper.generate_file_name(outputs)
+ outputs = oss_warpper.upload(oss_file_path, outputs)
+ return {"task": request.task, "image": outputs}
+
+def handle_molecule_property_prediction(request: TaskRequest, pipeline):
+ required_inputs = ["molecule", "dataset"]
+ molecule = IO_Reader.get_molecule(request.molecule)
+ dataset = IO_Reader.get_text(request.dataset)
+ outputs = pipeline.run(molecule=molecule, task=dataset.str)
+
+ #output = outputs[0][0].cpu()
+ #output = F.softmax(output, dim=0).tolist()
+ return {"task": request.task, "model": request.model, "score": outputs[0][0]}
+
+def handle_protein_binding_site_prediction(request: TaskRequest, pipeline):
+ required_inputs = ["protein"]
+ protein = IO_Reader.get_protein(request.protein)
+ outputs = pipeline.run(protein=protein)
+ output = outputs[1][0]
+ pocket_preview = str(outputs[0][0])
+ return {"task": request.task, "model": request.model, "pocket": output, "pocket_preview": pocket_preview}
+
+def handle_protein_folding(request: TaskRequest, pipeline):
+ required_inputs = ["protein"]
+ protein = IO_Reader.get_protein(request.protein)
+ outputs = pipeline.run(protein=protein)
+ protein = outputs[1][0]
+ return {"task": request.task, "model": request.model, "protein": protein}
+
+
+# Handlers for web_search
+async def handle_molecule_name_request(request: SearchRequest, requester):
+ outputs = await requester.run(request.query)
+ smiles = outputs[0][0].smiles
+ output = outputs[1][0]
+ return {"task": request.task, "molecule": output, "molecule_preview": smiles}
+
+def handle_web_search(request: SearchRequest, requester):
+ outputs = requester.run(request.query)
+ outputs = outputs[0][0]
+ return {"task": request.task, "text": outputs}
+
+async def handle_molecule_structure_request(request: SearchRequest, requester):
+ molecule = IO_Reader.get_molecule(request.molecule)
+ threshold = request.threshold
+ outputs = await requester.run(molecule, threshold=float(threshold), max_records=1)
+ # Pick a random molecule
+ index = random.randint(0, len(outputs[1])-1)
+ molecule = outputs[1][index]
+ molecule_preview = outputs[0][index].smiles
+ return {"task": request.task, "molecule": molecule, "molecule_preview": molecule_preview}
+
+
+async def handle_protein_uniprot_request(request: SearchRequest, requester):
+ outputs = await requester.run(request.query)
+ outputs = outputs[1][0]
+ protein = IO_Reader.get_protein(outputs)
+ protein_preview = str(protein)
+ return {"task": request.task, "protein": outputs, "protein_preview": protein_preview}
+
+
+async def handle_protein_pdb_request(request: SearchRequest, requester):
+ outputs = await requester.run(request.query)
+ outputs = outputs[1][0]
+ protein = IO_Reader.get_protein(outputs)
+ protein_preview = str(protein)
+ return {"task": request.task, "protein": outputs, "protein_preview": protein_preview}
+
+
+def handle_mutation_explanation(request: TaskRequest, pipeline):
+ required_inputs = ["protein", "mutation"]
+ mutation = request.mutation
+ protein = IO_Reader.get_protein(request.protein)
+ outputs = pipeline.run(protein=protein, mutation=mutation)
+ output = outputs[0][0]
+ return {"task": request.task, "model":request.model, "text": output}
+
+
+def handle_mutation_engineering(request: TaskRequest, pipeline):
+ required_inputs = ["protein", "text"]
+ protein = IO_Reader.get_protein(request.protein)
+ text = IO_Reader.get_text(request.text)
+ outputs = pipeline.run(protein=protein, text=text)
+ mutation_list = copy.deepcopy(outputs[0][0][:50])
+ mutation = random.choice(outputs[0][0])
+ converter = MutationToSequence()
+ outputs = converter.run([protein], [mutation])
+ protein = outputs[1][0]
+ protein_preview = outputs[0][0].sequence
+ return {"task": request.task, "model":request.model, "mutation": mutation_list, "protein": protein, "protein_preview": protein_preview}
+
+
+def handle_pocket_molecule_docking(request: TaskRequest, pipeline):
+ required_inputs = ["pocket", "molecule"]
+ pocket = Pocket.from_binary_file(request.pocket)
+ molecule = IO_Reader.get_molecule(request.molecule)
+ outputs = pipeline.run(pocket=pocket, molecule=molecule)
+ output = outputs[1][0]
+ return {"task": request.task, "model":request.model, "molecule": output}
+
+
+def handle_protein_molecule_docking_score(request: TaskRequest, pipeline):
+ required_inputs = ["protein", "molecule"]
+ protein = IO_Reader.get_protein(request.protein)
+ molecule = IO_Reader.get_molecule(request.molecule)
+ outputs = pipeline.run(protein=protein, molecule=molecule)
+ output = outputs[0][0]
+ return {"task": request.task, "model":request.model, "score": str(output)}
+
+
+def handle_visualize_protein(request: TaskRequest, pipeline):
+ required_inputs = ["protein"]
+ protein = IO_Reader.get_protein(request.protein)
+ vis_process = [
+ "python3", "./open_biomed/core/visualize.py",
+ "--task", "visualize_protein",
+ "--protein_config", request.visualize,
+ "--save_output_filename", "./tmp/protein_visualization_file.txt",
+ "--protein", request.protein]
+ subprocess.Popen(vis_process).communicate()
+ outputs = open("./tmp/protein_visualization_file.txt", "r").read()
+ oss_file_path = oss_warpper.generate_file_name(outputs)
+ outputs = oss_warpper.upload(oss_file_path, outputs)
+ return {"task": request.task, "image": outputs}
+
+
+def handle_visualize_protein_pocket(request: TaskRequest, pipeline):
+ required_inputs = ["protein", "pocket"]
+ protein = IO_Reader.get_protein(request.protein)
+ pocket = IO_Reader.get_pocket(request.pocket)
+ vis_process = [
+ "python3", "./open_biomed/core/visualize.py",
+ "--task", "visualize_protein_pocket",
+ "--save_output_filename", "./tmp/protein_pocket_visualization_file.txt",
+ "--protein", request.protein,
+ "--pocket", request.pocket]
+ subprocess.Popen(vis_process).communicate()
+ outputs = open("./tmp/protein_pocket_visualization_file.txt", "r").read()
+ oss_file_path = oss_warpper.generate_file_name(outputs)
+ outputs = oss_warpper.upload(oss_file_path, outputs)
+ return {"task": request.task, "image": outputs}
+
+
+def handle_export_molecule(request: TaskRequest, pipeline):
+ required_inputs = ["molecule"]
+ molecule = IO_Reader.get_molecule(request.molecule)
+ files = pipeline.run([molecule])
+ oss_file_path = oss_warpper.generate_file_name(files[0])
+ outputs = oss_warpper.upload(oss_file_path, files[0])
+ return {"task": request.task, "file": outputs}
+
+
+def handle_export_protein(request: TaskRequest, pipeline):
+ required_inputs = ["protein"]
+ protein = IO_Reader.get_protein(request.protein)
+ files = pipeline.run([protein])
+ oss_file_path = oss_warpper.generate_file_name(files[0])
+ outputs = oss_warpper.upload(oss_file_path, files[0])
+ return {"task": request.task, "file": outputs}
+
+
+def handle_import_pocket(request: TaskRequest, pipeline):
+ required_inputs = ["protein", "indices"]
+ protein = IO_Reader.get_protein(request.protein)
+ indices = [int(i) - 1 for i in request.indices.split(";")]
+ pockets, files = pipeline.run([protein], [indices])
+ return {"task": request.task, "pocket": files[0], "pocket_preview": str(pockets[0])}
+
+# 25
+def handle_molecule_similarity(request: TaskRequest, pipeline):
+ required_inputs = ["molecule_1", "molecule_2"]
+ molecule_1 = IO_Reader.get_molecule(request.molecule_1)
+ molecule_2 = IO_Reader.get_molecule(request.molecule_2)
+ outputs = pipeline.run(molecule_1=molecule_1, molecule_2=molecule_2)
+ return {"task": request.task, "model":request.model, "similarity": outputs}
+
+# 26
+def handle_molecule_property_calculation(request: TaskRequest, pipeline):
+ required_inputs = ["molecule", "property"]
+ molecule = IO_Reader.get_molecule(request.molecule)
+ property = request.property
+ outputs = pipeline.run(molecule=molecule, property=property)
+ return {"task": request.task, "model":request.model, "score": round(outputs, 5)}
+
+
+TASK_CONFIGS = [
+ {
+ "task_name": "text_based_molecule_editing",
+ "required_inputs": ["molecule", "text"],
+ "pipeline_key": "text_based_molecule_editing",
+ "handler_function": handle_text_based_molecule_editing,
+ "is_async": False
+ },
+ {
+ "task_name": "structure_based_drug_design",
+ "required_inputs": ["pocket"],
+ "pipeline_key": "structure_based_drug_design",
+ "handler_function": handle_structure_based_drug_design,
+ "is_async": False
+ },
+ {
+ "task_name": "molecule_question_answering",
+ "required_inputs": ["text", "molecule"],
+ "pipeline_key": "molecule_question_answering",
+ "handler_function": handle_molecule_question_answering,
+ "is_async": False
+ },
+ {
+ "task_name": "protein_question_answering",
+ "required_inputs": ["text", "protein"],
+ "pipeline_key": "protein_question_answering",
+ "handler_function": handle_protein_question_answering,
+ "is_async": False
+ },
+ {
+ "task_name": "visualize_molecule",
+ "required_inputs": ["visualize", "molecule"],
+ "pipeline_key": "visualize_molecule",
+ "handler_function": handle_visualize_molecule,
+ "is_async": False
+ },
+ {
+ "task_name": "visualize_complex",
+ "required_inputs": ["protein", "molecule"],
+ "pipeline_key": "visualize_complex",
+ "handler_function": handle_visualize_complex,
+ "is_async": False
+ },
+ {
+ "task_name": "visualize_protein",
+ "required_inputs": ["visualize", "protein"],
+ "pipeline_key": "visualize_protein",
+ "handler_function": handle_visualize_protein,
+ "is_async": False
+ },
+ {
+ "task_name": "visualize_protein_pocket",
+ "required_inputs": ["protein", "pocket"],
+ "pipeline_key": "visualize_protein_pocket",
+ "handler_function": handle_visualize_protein_pocket,
+ "is_async": False
+ },
+ {
+ "task_name": "molecule_property_prediction",
+ "required_inputs": ["molecule", "dataset"],
+ "pipeline_key": "molecule_property_prediction",
+ "handler_function": handle_molecule_property_prediction,
+ "is_async": False
+ },
+ {
+ "task_name": "protein_binding_site_prediction",
+ "required_inputs": ["protein"],
+ "pipeline_key": "protein_binding_site_prediction",
+ "handler_function": handle_protein_binding_site_prediction,
+ "is_async": False
+ },
+ {
+ "task_name": "protein_folding",
+ "required_inputs": ["protein"],
+ "pipeline_key": "protein_folding",
+ "handler_function": handle_protein_folding,
+ "is_async": False
+ },
+ {
+ "task_name": "molecule_name_request",
+ "required_inputs": ["query"],
+ "pipeline_key": "molecule_name_request",
+ "handler_function": handle_molecule_name_request,
+ "is_async": True
+ },
+ {
+ "task_name": "web_search",
+ "required_inputs": ["query"],
+ "pipeline_key": "web_search",
+ "handler_function": handle_web_search,
+ "is_async": False
+ },
+ {
+ "task_name": "molecule_structure_request",
+ "required_inputs": ["molecule", "threshold"],
+ "pipeline_key": "molecule_structure_request",
+ "handler_function": handle_molecule_structure_request,
+ "is_async": True
+ },
+ {
+ "task_name": "protein_uniprot_request",
+ "required_inputs": ["query"],
+ "pipeline_key": "protein_uniprot_request",
+ "handler_function": handle_protein_uniprot_request,
+ "is_async": True
+ },
+ {
+ "task_name": "protein_pdb_request",
+ "required_inputs": ["query"],
+ "pipeline_key": "protein_pdb_request",
+ "handler_function": handle_protein_pdb_request,
+ "is_async": True
+ },
+ {
+ "task_name": "mutation_explanation",
+ "required_inputs": ["mutation", "protein"],
+ "pipeline_key": "mutation_explanation",
+ "handler_function": handle_mutation_explanation,
+ "is_async": False
+ },
+ {
+ "task_name": "mutation_engineering",
+ "required_inputs": ["text", "protein"],
+ "pipeline_key": "mutation_engineering",
+ "handler_function": handle_mutation_engineering,
+ "is_async": False
+ },
+ {
+ "task_name": "pocket_molecule_docking",
+ "required_inputs": ["pocket", "molecule"],
+ "pipeline_key": "pocket_molecule_docking",
+ "handler_function": handle_pocket_molecule_docking,
+ "is_async": False
+ },
+ {
+ "task_name": "protein_molecule_docking_score",
+ "required_inputs": ["protein", "molecule"],
+ "pipeline_key": "protein_molecule_docking_score",
+ "handler_function": handle_protein_molecule_docking_score,
+ "is_async": False
+ },
+ {
+ "task_name": "export_molecule",
+ "required_inputs": ["molecule"],
+ "pipeline_key": "export_molecule",
+ "handler_function": handle_export_molecule,
+ "is_async": False
+ },
+ {
+ "task_name": "export_protein",
+ "required_inputs": ["protein"],
+ "pipeline_key": "export_protein",
+ "handler_function": handle_export_protein,
+ "is_async": False
+ },
+ {
+ "task_name": "import_pocket",
+ "required_inputs": ["pocket", "indices"],
+ "pipeline_key": "import_pocket",
+ "handler_function": handle_import_pocket,
+ "is_async": False
+ },
+ {
+ "task_name": "molecule_similarity", # 25
+ "required_inputs": ["molecule_1", "molecule_2"],
+ "pipeline_key": "molecule_similarity",
+ "handler_function": handle_molecule_similarity,
+ "is_async": False
+ },
+ {
+ "task_name": "molecule_property_calculation", # 25
+ "required_inputs": ["molecule", "property"],
+ "pipeline_key": "molecule_property_calculation",
+ "handler_function": handle_molecule_property_calculation,
+ "is_async": False
+ }
+
+
+]
+
+
+task_loader = TaskLoader()
+
+for task_config in TASK_CONFIGS:
+ task_loader.register_task(TaskConfig(
+ task_name=task_config["task_name"],
+ required_inputs=task_config["required_inputs"],
+ pipeline_key=task_config["pipeline_key"],
+ handler_function=task_config["handler_function"],
+ is_async=task_config["is_async"]
+ ))
+
+
+
+
+@app.post("/run_pipeline/")
+async def run_pipeline(request: TaskRequest):
+ task_name = request.task.lower()
+ logging.info(request)
+ try:
+ task_config = task_loader.get_task(task_name)
+ task_config.validate_inputs(request.model_dump())
+ pipeline = TOOLS[task_config.pipeline_key]
+ output = task_config.handler_function(request, pipeline)
+ return output
+ except Exception as e:
+ print(e)
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/web_search/")
+async def web_search(request: SearchRequest):
+ task_name = request.task.lower()
+
+ try:
+ task_config = task_loader.get_task(task_name)
+ task_config.validate_inputs(request.model_dump())
+ requester = TOOLS[task_config.pipeline_key]
+
+ if task_config.is_async:
+ output = await task_config.handler_function(request, requester)
+ else:
+ output = task_config.handler_function(request, requester)
+ return output
+ except Exception as e:
+ print(e)
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/healthz")
+def ping():
+ return "Service available"
+
+
+if __name__ == "__main__":
+ uvicorn.run(app, host="0.0.0.0", port=8082)
diff --git a/open_biomed/scripts/run_server_workflow.py b/open_biomed/scripts/run_server_workflow.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a052ff5f10fcf9b3b66d5515f5f9af28c342170
--- /dev/null
+++ b/open_biomed/scripts/run_server_workflow.py
@@ -0,0 +1,51 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import uvicorn
+import asyncio
+
+# import function
+from open_biomed.core.llm_request import ReportGeneratorSBDD, ReportGeneratorGeneral
+
+app = FastAPI()
+
+# Define the request body model
+class ReportRequest(BaseModel):
+ task: str
+ workflow: str
+ user_email: str
+ num_repeats: int
+
+@app.post("/run_workflow/")
+async def run_workflow(request: ReportRequest):
+ request = request.model_dump()
+ task = request["task"].lower()
+ try:
+ if task == "drug_design":
+ requester = ReportGeneratorSBDD()
+ else:
+ requester = ReportGeneratorGeneral()
+
+ required_inputs = ["workflow", "user_email", "num_repeats"]
+ if not all(key in request for key in required_inputs):
+ raise HTTPException(
+ status_code=400, detail="workflow config file is required for report generation task")
+
+ asyncio.create_task(requester.run(workflow=request["workflow"], user_email= request["user_email"], num_repeats=request["num_repeats"]))
+
+ return {"type": task+"_report", "content": "Workflow is still running...", "thinking": ""}
+ except Exception as e:
+ print(e)
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/healthz")
+def ping():
+ return "Service available"
+
+
+if __name__ == "__main__":
+ uvicorn.run(app, host="0.0.0.0", port=8083, limit_concurrency=2)
diff --git a/open_biomed/scripts/train.py b/open_biomed/scripts/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..9744501b58284a572bfc7279ba530d0e5a0d0561
--- /dev/null
+++ b/open_biomed/scripts/train.py
@@ -0,0 +1,9 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from open_biomed.core.pipeline import TrainValPipeline
+
+if __name__ == "__main__":
+ pipeline = TrainValPipeline()
+ pipeline.run()
\ No newline at end of file
diff --git a/open_biomed/tasks/__init__.py b/open_biomed/tasks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dabc48266c4777890de2cb3e2deef1eaa886e03
--- /dev/null
+++ b/open_biomed/tasks/__init__.py
@@ -0,0 +1,36 @@
+from typing import Optional
+
+from open_biomed.tasks.multi_modal_tasks.text_based_molecule_editing import TextMoleculeEditing
+from open_biomed.tasks.multi_modal_tasks.molecule_text_translation import MoleculeCaptioning
+from open_biomed.tasks.multi_modal_tasks.text_guided_molecule_generation import TextGuidedMoleculeGeneration
+from open_biomed.tasks.multi_modal_tasks.molecule_question_answering import MoleculeQA
+from open_biomed.tasks.multi_modal_tasks.protein_text_translation import TextBasedProteinGeneration
+from open_biomed.tasks.multi_modal_tasks.protein_question_answering import ProteinQA
+from open_biomed.tasks.multi_modal_tasks.mutation_text_translation import MutationExplanation, MutationEngineering
+from open_biomed.tasks.aidd_tasks.molecule_property_prediction import MoleculePropertyPrediction, MoleculePropertyPredictionRegression
+from open_biomed.tasks.aidd_tasks.protein_molecule_docking import PocketMoleculeDocking
+from open_biomed.tasks.aidd_tasks.structure_based_drug_design import StructureBasedDrugDesign
+from open_biomed.tasks.aidd_tasks.protein_folding import ProteinFolding
+from open_biomed.tasks.aidd_tasks.cell_annotation import CellAnnotation
+
+TASK_REGISTRY = {
+ "text_based_molecule_editing": TextMoleculeEditing,
+ "molecule_captioning": MoleculeCaptioning,
+ "text_guided_molecule_generation": TextGuidedMoleculeGeneration,
+ "molecule_question_answering": MoleculeQA,
+ "protein_question_answering": ProteinQA,
+ "text_based_protein_generation": TextBasedProteinGeneration,
+ "molecule_property_prediction": MoleculePropertyPrediction,
+ "molecule_property_prediction_regression": MoleculePropertyPredictionRegression,
+ "pocket_molecule_docking": PocketMoleculeDocking,
+ "structure_based_drug_design": StructureBasedDrugDesign,
+ "mutation_explanation": MutationExplanation,
+ "mutation_engineering": MutationEngineering,
+ "protein_folding": ProteinFolding,
+ "cell_annotation": CellAnnotation,
+}
+
+def check_compatible(task_name: str, dataset_name: Optional[str], model_name: Optional[str]) -> None:
+ # Check if the dataset and model supports the task
+ # If not, raise NotImplementedError
+ pass
\ No newline at end of file
diff --git a/open_biomed/tasks/aidd_tasks/__init__.py b/open_biomed/tasks/aidd_tasks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/aidd_tasks/cell_annotation.py b/open_biomed/tasks/aidd_tasks/cell_annotation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e09ba6dea76b06f0faecfc77b0b961b819920c55
--- /dev/null
+++ b/open_biomed/tasks/aidd_tasks/cell_annotation.py
@@ -0,0 +1,50 @@
+from typing import Dict, List, Tuple, Optional
+from typing_extensions import Any
+
+import json
+import logging
+import os
+import numpy as np
+import pytorch_lightning as pl
+from sklearn.metrics import (accuracy_score, average_precision_score,
+ roc_auc_score)
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import torch
+
+from open_biomed.data import molecule_fingerprint_similarity, check_identical_molecules
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class CellAnnotation(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage():
+ return "\n".join([
+ 'Cell annotation.',
+ 'Inputs: {"cell": a cell. "class_texts": text descriptions of the cell types.}',
+ "Outputs: A classification result in Int."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("cell_annotation", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("cell_annotation", model_cfg, train_cfg)
+
+ # @staticmethod
+ # def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ # return MoleculePropertyPredictionEvaluationCallback()
+
+ # @staticmethod
+ # def get_monitor_cfg() -> Struct:
+ # return Struct(
+ # name="val/roc_auc",
+ # output_str="-{val_roc_auc:.4f}",
+ # mode="max",
+ # )
diff --git a/open_biomed/tasks/aidd_tasks/drug_cell_response_prediction.py b/open_biomed/tasks/aidd_tasks/drug_cell_response_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/aidd_tasks/drug_drug_interaction.py b/open_biomed/tasks/aidd_tasks/drug_drug_interaction.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/aidd_tasks/molecule_design.py b/open_biomed/tasks/aidd_tasks/molecule_design.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/aidd_tasks/molecule_property_prediction.py b/open_biomed/tasks/aidd_tasks/molecule_property_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f814339d5ffcccf3ee7c4f1bc2b5203807ad7eb
--- /dev/null
+++ b/open_biomed/tasks/aidd_tasks/molecule_property_prediction.py
@@ -0,0 +1,326 @@
+from typing import Dict, List, Tuple, Optional
+from typing_extensions import Any
+
+import json
+import logging
+import os
+import numpy as np
+import pytorch_lightning as pl
+from sklearn.metrics import (accuracy_score, average_precision_score,
+ roc_auc_score)
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import torch
+from scipy.stats import spearmanr
+
+
+from open_biomed.data import molecule_fingerprint_similarity, check_identical_molecules
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class MoleculePropertyPrediction(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage():
+ return "\n".join([
+ 'Molecular property prediction.',
+ 'Inputs: {"molecule": a small molecule}',
+ "Outputs: A float number in [0, 1] indicating the likeness of the molecule to exhibit certain properties."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("molecule_property_prediction", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("molecule_property_prediction", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return MoleculePropertyPredictionEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/roc_auc",
+ output_str="-{val_roc_auc:.4f}",
+ mode="max",
+ )
+
+
+class MoleculePropertyPredictionEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+ self.eval_metric = roc_auc_score
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = '\n'.join([str(x) for x in self.eval_dataset.labels[i]])
+ logging.info(f"Original: {self.eval_dataset.molecules[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+
+ y_true = np.squeeze(np.array(self.eval_dataset.labels), axis=1)
+
+ self.outputs = [torch.tensor(x) for x in self.outputs]
+
+ # y_scores = torch.unsqueeze(torch.tensor(self.outputs), dim=1).numpy()
+ y_scores_tensor = torch.stack(self.outputs, dim=0)
+ y_scores = y_scores_tensor.cpu().numpy()
+
+ output_str = "\t".join(["roc_auc"]) + "\n"
+
+ roc_list = []
+ for i in range(y_true.shape[1]):
+ # AUC is only defined when there is at least one positive data.
+ if np.sum(y_true[:, i] == 1) > 0 and np.sum(y_true[:, i] == -1) > 0:
+ is_valid = y_true[:, i] ** 2 > 0
+ roc_list.append(self.eval_metric((y_true[is_valid, i] + 1) / 2, y_scores[is_valid, i]))
+ else:
+ print('{} is invalid'.format(i))
+
+ if len(roc_list) < y_true.shape[1]:
+ logging.info(len(roc_list))
+ logging.info('Some target is missing!')
+ logging.info('Missing ratio: %f' %(1 - float(len(roc_list)) / y_true.shape[1]))
+
+ roc_auc = sum(roc_list) / len(roc_list)
+
+ logging.info(f"{self.status}/roc_auc: {roc_auc:.4f}")
+
+ output_str += "\t".join([f"{roc_auc:.4f}"]) + "\n"
+
+ # 更新监控值
+ pl_module.log_dict({f"{self.status}/roc_auc": roc_auc})
+
+ output_path = os.path.join(trainer.default_root_dir, f"{self.status}_outputs", f"epoch{pl_module.current_epoch}")
+
+ # 保存测试结果
+ if trainer.is_global_zero:
+ with open(output_path + "_outputs.txt", "w") as f:
+ f.write(output_str)
+
+ def on_test_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0,
+ ) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_test_epoch_start(trainer, pl_module)
+ self.status = "test"
+ self.outputs = []
+ self.eval_dataset = trainer.test_dataloaders.dataset
+
+ def on_test_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
+
+
+class MoleculePropertyPredictionRegression(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage():
+ return "\n".join([
+ 'Molecular property prediction.',
+ 'Inputs: {"molecule": a small molecule}',
+ "Outputs: A float number in indicating the regression result of the molecule's certain properties."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("molecule_property_prediction", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("molecule_property_prediction", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ # TODO：传个参数切换 回归与分类任务的评估方法
+ return MoleculePropertyPredictionRegressionEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/mae",
+ output_str="-{mae:.4f}",
+ mode="min",
+ )
+ # return Struct(
+ # name="val/spearman",
+ # output_str="-{val_spearman:.4f}",
+ # mode="max",
+ # )
+
+class MoleculePropertyPredictionRegressionEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+ # self.eval_metric = roc_auc_score
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = '\n'.join([str(x) for x in self.eval_dataset.labels[i]])
+ logging.info(f"Original: {self.eval_dataset.molecules[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+
+ y_true = np.squeeze(np.array(self.eval_dataset.labels), axis=1)
+
+ # y_scores = torch.unsqueeze(torch.tensor(self.outputs), dim=1).numpy()
+ # print(self.outputs)
+ # print(y_true)
+ # print(y_true.shape)
+ # print(len(self.outputs))
+
+ tensors = [torch.tensor(x) for x in self.outputs]
+ y_pred_tensor = torch.stack(tensors, dim=0)
+ y_pred = y_pred_tensor.cpu().numpy()
+
+ # Prepare metrics for regression
+ output_str = "\t".join(["rmse", "mae", "r2", "spearman"]) + "\n"
+
+ rmse_list = []
+ mae_list = []
+ r2_list = []
+ spearman_list = []
+
+ for i in range(y_true.shape[1]):
+ is_valid = ~np.isnan(y_true[:, i]) # Check for valid targets
+ y_true_col = y_true[is_valid, i]
+ y_pred_col = y_pred[is_valid, i]
+
+ if len(y_true_col) > 0: # Only calculate if we have valid samples
+ # Calculate RMSE
+ rmse = np.sqrt(np.mean((y_true_col - y_pred_col) ** 2))
+ rmse_list.append(rmse)
+
+ # Calculate MAE
+ mae = np.mean(np.abs(y_true_col - y_pred_col))
+ mae_list.append(mae)
+
+ # Calculate R²
+ ss_res = np.sum((y_true_col - y_pred_col) ** 2)
+ ss_tot = np.sum((y_true_col - np.mean(y_true_col)) ** 2)
+ r2 = 1 - (ss_res / (ss_tot + 1e-10)) # Small epsilon to avoid division by zero
+ r2_list.append(r2)
+
+ # Calculate Spearman correlation
+ spearman_corr, _ = spearmanr(y_true_col, y_pred_col)
+ spearman_list.append(spearman_corr)
+ else:
+ print(f'Column {i} has no valid targets')
+
+ if len(rmse_list) < y_true.shape[1]:
+ logging.info(f'Valid targets count: {len(rmse_list)}/{y_true.shape[1]}')
+ logging.info('Some target is missing!')
+ logging.info('Missing ratio: %f' % (1 - float(len(rmse_list)) / y_true.shape[1]))
+
+ # Calculate average metrics
+ avg_rmse = np.mean(rmse_list) if rmse_list else np.nan
+ avg_mae = np.mean(mae_list) if mae_list else np.nan
+ avg_r2 = np.mean(r2_list) if r2_list else np.nan
+ avg_spearman = np.mean(spearman_list) if spearman_list else np.nan
+
+ logging.info(f"{self.status}/rmse: {avg_rmse:.4f}")
+ logging.info(f"{self.status}/mae: {avg_mae:.4f}")
+ logging.info(f"{self.status}/r2: {avg_r2:.4f}")
+ logging.info(f"{self.status}/spearman: {avg_spearman:.4f}")
+
+ output_str += "\t".join([f"{avg_rmse:.4f}", f"{avg_mae:.4f}", f"{avg_r2:.4f}", f"{avg_spearman:.4f}"]) + "\n"
+
+ # Log metrics
+ pl_module.log_dict({
+ f"{self.status}/rmse": avg_rmse,
+ f"{self.status}/mae": avg_mae,
+ f"{self.status}/r2": avg_r2,
+ f"{self.status}/spearman": avg_spearman
+ })
+
+ # Save results
+ output_path = os.path.join(trainer.default_root_dir, f"{self.status}_outputs", f"epoch{pl_module.current_epoch}")
+ if trainer.is_global_zero:
+ with open(output_path + "_outputs.txt", "w") as f:
+ f.write(output_str)
+
+
+
+ def on_test_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0,
+ ) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_test_epoch_start(trainer, pl_module)
+ self.status = "test"
+ self.outputs = []
+ self.eval_dataset = trainer.test_dataloaders.dataset
+
+ def on_test_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
diff --git a/open_biomed/tasks/aidd_tasks/protein_design.py b/open_biomed/tasks/aidd_tasks/protein_design.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/aidd_tasks/protein_folding.py b/open_biomed/tasks/aidd_tasks/protein_folding.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f75e50d9d0b65b1656b907bc9fc93430169401e
--- /dev/null
+++ b/open_biomed/tasks/aidd_tasks/protein_folding.py
@@ -0,0 +1,47 @@
+from typing import Optional
+
+import pytorch_lightning as pl
+
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class ProteinFolding(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage():
+ return "\n".join([
+ 'Protein folding prediction.',
+ 'Inputs: {"protein": a protein sequence}',
+ "Outputs: A protein object with 3D structure available."
+ ])
+
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("protein_folding", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("protein_folding", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return FoldingEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/rmsd",
+ output_str="-{val_rmsd:.4f}",
+ mode="min",
+ )
+
+class FoldingEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+
+ # TODO: Implement the evaluation logic
\ No newline at end of file
diff --git a/open_biomed/tasks/aidd_tasks/protein_molecule_docking.py b/open_biomed/tasks/aidd_tasks/protein_molecule_docking.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d98a34783dbc593125bb0f619b3257de3bebf9b
--- /dev/null
+++ b/open_biomed/tasks/aidd_tasks/protein_molecule_docking.py
@@ -0,0 +1,282 @@
+from typing import List, Optional, Union, Dict, Any
+
+import contextlib
+import copy
+import numpy as np
+import os
+from tqdm import tqdm
+import sys
+import pytorch_lightning as pl
+
+from open_biomed.core.tool import Tool
+from open_biomed.data import Molecule, Protein, calc_mol_rmsd, mol_array_to_conformer
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class PocketMoleculeDocking(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Ligand-pocket docking.',
+ 'Inputs: {"molecule": the ligand, "pocket": the pocket}',
+ "Outputs: A new molecule with 3D coordinates indicating the binding pose."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("pocket_molecule_docking", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("pocket_molecule_docking", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ if callback_cfg is None or getattr(callback_cfg, "rmsd", False):
+ return DockingRMSDEvaluationCallback()
+ else:
+ try:
+ return DockingPoseBustersEvaluationCallback()
+ except ImportError:
+ return DockingRMSDEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/rmsd_mean",
+ output_str="-{val_rmsd_mean:.4f}",
+ mode="min",
+ )
+
+def pbcheck_single(mol_pred: List[Molecule], mol_gt: Molecule, protein: Protein, buster: object) -> List[float]:
+ pdb_path = protein.save_pdb()
+ for mol in mol_pred:
+ mol._add_rdmol()
+ buster_result = buster.bust([mol.rdmol for mol in mol_pred], mol_gt.rdmol, pdb_path)
+ buster_result = buster_result.reset_index().iloc[0].to_dict()
+ return buster_result
+
+def aggregate_pb_results(pb_results: List[Dict[str, Any]]) -> Dict[str, Any]:
+ pbvalid_mol_keys = [
+ "mol_pred_loaded",
+ "mol_cond_loaded",
+ "sanitization",
+ "inchi_convertible",
+ "all_atoms_connected",
+ "bond_lengths",
+ "bond_angles",
+ "internal_steric_clash",
+ "aromatic_ring_flatness",
+ "double_bond_flatness",
+ "internal_energy",
+ ]
+
+ pbvalid_dock_keys = [
+ "protein-ligand_maximum_distance",
+ "minimum_distance_to_protein",
+ "minimum_distance_to_organic_cofactors",
+ "minimum_distance_to_inorganic_cofactors",
+ "minimum_distance_to_waters",
+ "volume_overlap_with_protein",
+ "volume_overlap_with_organic_cofactors",
+ "volume_overlap_with_inorganic_cofactors",
+ "volume_overlap_with_waters",
+ ]
+
+ pbvalid_mol = True
+ for key in pbvalid_mol_keys:
+ pbvalid_mol &= pb_results[0][key]
+ pbvalid_dock = True
+ for key in pbvalid_dock_keys:
+ pbvalid_dock &= pb_results[0][key]
+ return {
+ "pbvalid_mol": pbvalid_mol,
+ "pbvalid_dock": pbvalid_dock,
+ "pbvalid": pbvalid_mol and pbvalid_dock,
+ }
+
+class DockingRMSDEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.rmsds = []
+ self.eval_dataset = None
+
+ def on_validation_batch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule, outputs: Dict[str, Any], batch: Dict[str, Any], batch_idx: int, dataloader_idx: int=0) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ all_rmsd = []
+ for i in tqdm(range(len(self.outputs)), desc="Calculating docking RMSD"):
+ # The decoded molecule may be different due to post-processing such as kekulization, so we only update the conformer.
+ self.outputs[i].rdmol = copy.deepcopy(self.eval_dataset.molecules[i].rdmol)
+ self.outputs[i].rdmol.RemoveAllConformers()
+ self.outputs[i].rdmol.AddConformer(mol_array_to_conformer(self.outputs[i].conformer))
+ rmsd = calc_mol_rmsd(self.outputs[i], self.eval_dataset.molecules[i])
+ all_rmsd.append(rmsd)
+ self.rmsds.append(all_rmsd)
+ pl_module.log(f"{self.status}/rmsd_mean", np.mean(all_rmsd))
+ pl_module.log(f"{self.status}/rmsd_median", np.median(all_rmsd))
+ pl_module.log(f"{self.status}/rmsd < 2Å (%)", np.mean([rmsd < 2 for rmsd in all_rmsd]))
+
+ def on_test_batch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule, outputs: Dict[str, Any], batch: Dict[str, Any], batch_idx: int, dataloader_idx: int=0) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ # For docking, we may generate multiple poses.
+ super().on_test_epoch_start(trainer, pl_module)
+ if getattr(self, "status", None) != "test":
+ self.status = "test"
+ self.rmsds = []
+ self.eval_dataset = trainer.test_dataloaders.dataset
+ self.outputs = []
+
+ def on_test_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
+
+class DockingPoseBustersEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.rmsds = []
+ self.pbresults = {}
+ self.eval_dataset = None
+ try:
+ from posebusters import PoseBusters
+ self.buster = PoseBusters()
+ except ImportError:
+ raise ImportError("PoseBusters not installed. Please install it using `pip install posebusters==0.3.1`.")
+
+ def on_validation_batch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule, outputs: Dict[str, Any], batch: Dict[str, Any], batch_idx: int, dataloader_idx: int=0) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ all_rmsd = []
+ all_pbresults = {}
+ for i in tqdm(range(len(self.outputs)), desc="Calculating docking RMSD"):
+ rmsd = calc_mol_rmsd(self.outputs[i], self.eval_dataset.molecules[i])
+ all_rmsd.append(rmsd)
+ pb_results = pbcheck_single(self.outputs[i], self.eval_dataset.molecules[i], self.eval_dataset.proteins[i], self.buster)
+ for key, value in pb_results.items():
+ if key not in all_pbresults:
+ all_pbresults[key] = []
+ all_pbresults[key].append(value)
+ agg = aggregate_pb_results(pb_results)
+ for key, value in agg.items():
+ if key not in all_pbresults:
+ all_pbresults[key] = []
+ all_pbresults[key].append(value)
+ if "pbvalid & rmsd < 2Å" not in all_pbresults:
+ all_pbresults["pbvalid & rmsd < 2Å"] = []
+ all_pbresults["pbvalid & rmsd < 2Å"].append(rmsd < 2 and agg["pbvalid"])
+ self.rmsds.append(all_rmsd)
+ for key, value in all_pbresults.items():
+ if key not in self.pbresults:
+ self.pbresults[key] = []
+ self.pbresults[key].append(value)
+ pl_module.log(f"{self.status}/{key}", np.mean(value))
+ pl_module.log(f"{self.status}/rmsd_mean", np.mean(all_rmsd))
+ pl_module.log(f"{self.status}/rmsd_median", np.median(all_rmsd))
+
+ def on_test_batch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule, outputs: Dict[str, Any], batch: Dict[str, Any], batch_idx: int, dataloader_idx: int=0) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ # For docking, we may generate multiple poses.
+ super().on_test_epoch_start(trainer, pl_module)
+ if getattr(self, "status", None) != "test":
+ self.status = "test"
+ self.rmsds = []
+ self.pbresults = {}
+ self.eval_dataset = trainer.test_dataloaders.dataset
+ self.outputs = []
+
+ def on_test_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
+
+class VinaDockTask(Tool):
+ def __init__(self, mode: str="dock") -> None:
+ self.mode = mode
+
+ python_path = sys.executable
+ conda_env_root = os.path.dirname(os.path.dirname(python_path))
+ self.pdb2pqr_path = os.path.join(conda_env_root, 'bin', 'pdb2pqr30')
+
+ def print_usage(self) -> str:
+ return "\n".join([
+ 'Ligand-receptor docking.',
+ 'Inputs: {"molecule": the ligand, "protein": the receptor}',
+ "Outputs: A float number indicating the AutoDockVina score of the binding."
+ ])
+
+ def run(self, molecule: Molecule, protein: Protein) -> Union[List[float], List[str]]:
+ sdf_file = molecule.save_sdf()
+ pdb_file = protein.save_pdb()
+ pos = np.array(molecule.conformer)
+ center = (pos.max(0) + pos.min(0)) / 2
+ size = pos.max(0) - pos.min(0) + 5
+ try:
+ from openbabel import pybel
+ from meeko import MoleculePreparation
+ import subprocess
+ from vina import Vina
+ import AutoDockTools
+
+ ob_mol = next(pybel.readfile("sdf", sdf_file))
+ lig_pdbqt = sdf_file.replace(".sdf", ".pdbqt")
+ if not os.path.exists(lig_pdbqt):
+ with open(os.devnull, 'w') as devnull:
+ with contextlib.redirect_stdout(devnull):
+ preparator = MoleculePreparation()
+ preparator.prepare(ob_mol.OBMol)
+ preparator.write_pdbqt_file(lig_pdbqt)
+
+ prot_pqr = pdb_file.replace(".pdb", ".pqr")
+ if not os.path.exists(prot_pqr):
+ subprocess.Popen([self.pdb2pqr_path,'--ff=AMBER', pdb_file, prot_pqr],
+ stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL).communicate()
+ prot_pdbqt = pdb_file.replace(".pdb", ".pdbqt")
+ if not os.path.exists(prot_pdbqt):
+ prepare_receptor = os.path.join(AutoDockTools.__path__[0], 'Utilities24/prepare_receptor4.py')
+ subprocess.Popen(['python3', prepare_receptor, '-r', prot_pqr, '-o', prot_pdbqt],
+ stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL).communicate()
+
+ v = Vina(sf_name='vina', seed=0, verbosity=0)
+ v.set_receptor(prot_pdbqt)
+ v.set_ligand_from_file(lig_pdbqt)
+ v.compute_vina_maps(center=center, box_size=size)
+ if self.mode == "min":
+ score = v.optimize()[0]
+ pose_file = f"./tmp/{molecule.name}_{protein.name}_pose"
+ with open(pose_file, "w") as f:
+ v.write_pose(pose_file, overwrite=True)
+ elif self.mode == 'dock':
+ v.dock(exhaustiveness=8, n_poses=1)
+ score = v.energies(n_poses=1)[0][0]
+ pose_file = "None"
+ elif self.mode == 'score':
+ score = v.score()[0]
+ pose_file = "None"
+ return [score], [pose_file]
+ except ImportError:
+ print("AutoDockVina not installed. This function return 0.0.")
+ return [0.0], ["0.0"]
\ No newline at end of file
diff --git a/open_biomed/tasks/aidd_tasks/protein_property_prediction.py b/open_biomed/tasks/aidd_tasks/protein_property_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/aidd_tasks/protein_protein_interaction.py b/open_biomed/tasks/aidd_tasks/protein_protein_interaction.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/aidd_tasks/structure_based_drug_design.py b/open_biomed/tasks/aidd_tasks/structure_based_drug_design.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e04717e5aeffe42e4dda954d5e98e531cb97162
--- /dev/null
+++ b/open_biomed/tasks/aidd_tasks/structure_based_drug_design.py
@@ -0,0 +1,273 @@
+from typing import Optional, Any, Dict, List
+
+import logging
+import numpy as np
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+from rdkit import Chem
+from rdkit.Chem import rdMolTransforms
+from scipy.spatial.distance import jensenshannon
+from tqdm import tqdm
+
+from open_biomed.data import Molecule, Protein
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class StructureBasedDrugDesign(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Structure-based drug design.',
+ 'Inputs: {"pocket": a protein pocket}',
+ "Outputs: A small molecule that is likely to bind with the pocket."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("structure_based_drug_design", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("structure_based_drug_design", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return StructureBasedDrugDesignEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/vina_score/median",
+ output_str="-{val_vina_score_med:.4f}",
+ mode="min",
+ )
+
+def calc_vina_molecule_metrics(molecule: Molecule, protein: Protein, calculate_vina_dock: bool=True) -> Dict[str, float]:
+ metrics = {}
+
+ # Vina scores
+ from open_biomed.tasks.aidd_tasks.protein_molecule_docking import VinaDockTask
+ modes = ["min", "dock", "score"] if calculate_vina_dock else ["min", "score"]
+ for mode in modes:
+ vina_task = VinaDockTask(mode=mode)
+ metrics[f"vina_{mode}"] = vina_task.run(molecule, protein)[0][0]
+
+ # Molecule property metrics
+ molecule._add_smiles()
+ metrics["completeness"] = 0 if "." in molecule.smiles else 1
+ metrics["qed"] = molecule.calc_qed()
+ metrics["sa"] = molecule.calc_sa()
+ metrics["logp"] = molecule.calc_logp()
+ metrics["lipinski"] = molecule.calc_lipinski()
+
+ # Success rate
+ if calculate_vina_dock:
+ metrics["success"] = 1 if metrics["vina_dock"] < -8.18 and metrics["qed"] > 0.25 and metrics["sa"] > 0.59 else 0
+ else:
+ metrics["success"] = 1 if metrics["vina_min"] < -8.18 and metrics["qed"] > 0.25 and metrics["sa"] > 0.59 else 0
+ return metrics
+
+BOND_TYPE_TO_ID = {
+ Chem.rdchem.BondType.UNSPECIFIED: 0,
+ Chem.rdchem.BondType.SINGLE: 1,
+ Chem.rdchem.BondType.DOUBLE: 2,
+ Chem.rdchem.BondType.TRIPLE: 3,
+ Chem.rdchem.BondType.AROMATIC: 4,
+}
+EVAL_BOND_TYPES = [
+ (6, 6, 1), # CC 716 29.6% 1.2857153558712793 1.696778883283098 0.004110635274118186
+ (6, 6, 4), # C:C 500 20.7% 1.2981754588686738 1.5429516779717267 0.002447762191030529
+ (6, 8, 1), # CO 336 13.9% 1.217717567891834 1.592581263775381 0.0037486369588354694
+ (6, 7, 1), # CN 245 10.1% 1.2412786652760066 1.609101379383609 0.0036782271410760246
+ (6, 7, 4), # C:N 213 8.8% 1.2781037555594505 1.4881754271876604 0.002100716716282098
+]
+EVAL_BOND_TYPES_STR = ["CC", "C:C", "CO", "CN", "C:N"]
+EVAL_BOND_ANGLES = [
+ (6, 1, 6, 1, 6), # CCC 521 18.1% 59.52230720788234 135.50315793532704 0.759808507274447
+ (6, 4, 6, 4, 6), # C:C:C 460 16.0% 101.54806405949785 127.54928623790771 0.2600122217840986
+ (6, 1, 6, 1, 8), # CCO 274 9.5% 57.19735111082594 136.5409407542893 0.7934358964346336
+]
+EVAL_BOND_ANGLES_STR = ["CCC", "C:C:C", "CCO"]
+
+def calc_bond_length_profile(molecule: Molecule) -> Dict[str, List[float]]:
+ distance = molecule.calc_distance()
+ bond_len_profile = {k: [] for k in EVAL_BOND_TYPES}
+ for bond in molecule.rdmol.GetBonds():
+ s_sym = bond.GetBeginAtom().GetAtomicNum()
+ t_sym = bond.GetEndAtom().GetAtomicNum()
+ s_idx, t_idx = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
+ bond_type = BOND_TYPE_TO_ID[bond.GetBondType()]
+ if s_idx > t_idx:
+ s_idx, t_idx = t_idx, s_idx
+ if (s_sym, t_sym, bond_type) in EVAL_BOND_TYPES:
+ bond_len_profile[(s_sym, t_sym, bond_type)].append(distance[s_idx, t_idx])
+ return bond_len_profile
+
+def calc_bond_angle_profile(molecule: Molecule) -> Dict[str, List[float]]:
+ bond_angle_profile = {k: [] for k in EVAL_BOND_ANGLES}
+ for bond1 in molecule.rdmol.GetBonds():
+ atom1, atom2 = bond1.GetBeginAtom(), bond1.GetEndAtom()
+ for bond2 in atom2.GetBonds():
+ atom3 = bond2.GetOtherAtom(atom2)
+ if atom3.GetIdx() == atom1.GetIdx():
+ continue
+ try:
+ angle = rdMolTransforms.GetAngleDeg(molecule.rdmol.GetConformer(), atom1.GetIdx(), atom2.GetIdx(), atom3.GetIdx())
+ atom1_sym = atom1.GetAtomicNum()
+ atom2_sym = atom2.GetAtomicNum()
+ atom3_sym = atom3.GetAtomicNum()
+ bond1_type = BOND_TYPE_TO_ID[bond1.GetBondType()]
+ bond2_type = BOND_TYPE_TO_ID[bond2.GetBondType()]
+ if atom1_sym > atom3_sym:
+ atom1_sym, atom3_sym = atom3_sym, atom1_sym
+ bond1_type, bond2_type = bond2_type, bond1_type
+ if (atom1_sym, bond1_type, atom2_sym, bond2_type, atom3_sym) in EVAL_BOND_ANGLES:
+ bond_angle_profile[(atom1_sym, bond1_type, atom2_sym, bond2_type, atom3_sym)].append(angle)
+
+ except Exception as e:
+ logging.error(f"Error calculating bond angle for {molecule.name}: {e}")
+ continue
+
+ return bond_angle_profile
+
+class StructureBasedDrugDesignEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+
+ def init_metrics(self) -> None:
+ self.metrics = {"valid": []}
+ self.bond_len_profile_ref = {}
+ self.bond_len_profile_pred = {}
+ self.bond_angle_profile_ref = {}
+ self.bond_angle_profile_pred = {}
+ for elem in EVAL_BOND_TYPES:
+ self.bond_len_profile_ref[elem] = []
+ self.bond_len_profile_pred[elem] = []
+ for elem in EVAL_BOND_ANGLES:
+ self.bond_angle_profile_ref[elem] = []
+ self.bond_angle_profile_pred[elem] = []
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+ if batch_idx == 0:
+ for i in range(1):
+ logging.info(f"Generated Molecule: {self.outputs[i]}")
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ for i in tqdm(range(len(self.outputs)), desc="Calculating SBDD metrics"):
+ if self.outputs[i] is None:
+ self.metrics["valid"].append(0)
+ continue
+ self.metrics["valid"].append(1)
+ cur_metrics = calc_vina_molecule_metrics(self.outputs[i], self.eval_dataset.proteins[i])
+ for k, v in cur_metrics.items():
+ if not k in self.metrics:
+ self.metrics[k] = []
+ self.metrics[k].append(v)
+
+ # Calculate bond distance & angle profiles
+ cur_len_profile_ref = calc_bond_length_profile(self.eval_dataset.molecules[i])
+ for k, v in cur_len_profile_ref.items():
+ self.bond_len_profile_ref[k].extend(v)
+ cur_len_profile_pred = calc_bond_length_profile(self.outputs[i])
+ for k, v in cur_len_profile_pred.items():
+ self.bond_len_profile_pred[k].extend(v)
+
+ cur_angle_profile_ref = calc_bond_angle_profile(self.eval_dataset.molecules[i])
+ for k, v in cur_angle_profile_ref.items():
+ self.bond_angle_profile_ref[k].extend(v)
+ cur_angle_profile_pred = calc_bond_angle_profile(self.outputs[i])
+ for k, v in cur_angle_profile_pred.items():
+ self.bond_angle_profile_pred[k].extend(v)
+
+ # Evaluate clash
+ """
+ try:
+ from posecheck import PoseCheck
+ pc = PoseCheck()
+ pdb_file = self.eval_dataset.pockets[i].save_pdb()
+ pc.load_protein_from_pdb(pdb_file)
+ pc.load_ligands_from_mols([self.outputs[i].rdmol])
+ clash = pc.calculate_clashes()[0]
+ if "clash_num" not in self.metrics:
+ self.metrics["clash_num"] = []
+ self.metrics["atom_num"] = []
+ self.metrics["clash_num"].append(clash)
+ self.metrics["atom_num"].append(self.outputs[i].get_num_atoms())
+ except ImportError:
+ logging.error("posecheck is not installed. Please install it following README.md.")
+ """
+
+ # WARNING: the results are averaged over all test repeats, so only the last repeat should be used
+ output_metrics = {}
+ for vina_metrics in ["vina_min", "vina_dock", "vina_score"]:
+ output_metrics[f"{self.status}/{vina_metrics}/median"] = np.median(self.metrics[vina_metrics])
+ output_metrics[f"{self.status}/{vina_metrics}/mean"] = np.mean(self.metrics[vina_metrics])
+ for metric in ["valid", "qed", "sa", "logp", "lipinski", "success"]:
+ output_metrics[f"{self.status}/{metric}"] = np.mean(self.metrics[metric])
+ # output_metrics[f"{self.status}/clash_ratio_atom"] = np.sum(self.metrics["clash_num"]) / np.sum(self.metrics["atom_num"])
+ # output_metrics[f"{self.status}/clash_ratio_molecule"] = np.mean(np.array(self.metrics["clash_num"]) > 0)
+
+ for i, bond_type in enumerate(EVAL_BOND_TYPES):
+ bond_len_dist_ref = np.histogram(self.bond_len_profile_ref[bond_type], bins=np.arange(1.1, 1.7000001, 0.005), density=True)[0]
+ bond_len_dist_pred = np.histogram(self.bond_len_profile_pred[bond_type], bins=np.arange(1.1, 1.7000001, 0.005), density=True)[0]
+ jsd = jensenshannon(bond_len_dist_ref, bond_len_dist_pred)
+ output_metrics[f"{self.status}/JSD_bond_len_{EVAL_BOND_TYPES_STR[i]}"] = 1 if np.isnan(jsd) else jsd
+
+ for i, bond_angle in enumerate(EVAL_BOND_ANGLES):
+ bond_angle_dist_ref = np.histogram(self.bond_angle_profile_ref[bond_angle], bins=np.arange(100, 140.01, 0.25), density=True)[0]
+ bond_angle_dist_pred = np.histogram(self.bond_angle_profile_pred[bond_angle], bins=np.arange(100, 140.01, 0.25), density=True)[0]
+ jsd = jensenshannon(bond_angle_dist_ref, bond_angle_dist_pred)
+ output_metrics[f"{self.status}/JSD_bond_angle_{EVAL_BOND_ANGLES_STR[i]}"] = 1 if np.isnan(jsd) else jsd
+
+ pl_module.log_dict(output_metrics)
+
+ def on_test_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0,
+ ) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ # For SBDD, we perform test multiple times
+ super().on_test_epoch_start(trainer, pl_module)
+
+ if getattr(self, "status", None) != "test":
+ # First time test, initialize the stored results
+ self.status = "test"
+ self.init_metrics()
+ self.eval_dataset = trainer.test_dataloaders.dataset
+ self.outputs = []
+
+
+ def on_test_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
+
diff --git a/open_biomed/tasks/base_task.py b/open_biomed/tasks/base_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb51b6d929025d83c24dc3886b8a46d499df3f5b
--- /dev/null
+++ b/open_biomed/tasks/base_task.py
@@ -0,0 +1,164 @@
+# Three major responsibilities of a Task module:
+# Corresponding Datasets
+# TaskModel Wrapper: defining training and validation step, call appropreiate Model functions
+# Callbacks: sampling / validation
+
+from abc import ABC, abstractmethod, abstractstaticmethod
+from pytorch_lightning.utilities.types import TRAIN_DATALOADERS, EVAL_DATALOADERS, STEP_OUTPUT
+from typing import Dict, Tuple, Optional
+from typing_extensions import Any
+
+import os
+import pytorch_lightning as pl
+import torch
+from torch.utils.data import DataLoader
+
+from open_biomed.models import MODEL_REGISTRY
+from open_biomed.datasets import DATASET_REGISTRY
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class ModelWrapper(pl.LightningModule, ABC):
+ def __init__(self, *args: Any, **kwargs: Any) -> None:
+ super().__init__(*args, **kwargs)
+
+ @abstractmethod
+ def get_featurizer(self) -> Tuple[Featurizer, Collator]:
+ raise NotImplementedError
+
+class BaseTask(ABC):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @abstractstaticmethod
+ def print_usage():
+ raise NotImplementedError
+
+ @abstractstaticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ raise NotImplementedError
+
+ @abstractstaticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> ModelWrapper:
+ raise NotImplementedError
+
+ @abstractstaticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ raise NotImplementedError
+
+ @abstractstaticmethod
+ def get_monitor_cfg() -> Struct:
+ raise NotImplementedError
+
+class DefaultDataModule(pl.LightningDataModule):
+ def __init__(self, task: str,dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> None:
+ super(DefaultDataModule, self).__init__()
+ dataset = DATASET_REGISTRY[task][dataset_cfg.name](dataset_cfg, featurizer)
+ train, valid, test = dataset.split()
+ self.train_loader = DataLoader(
+ dataset=train,
+ batch_size=dataset_cfg.batch_size_train,
+ shuffle=True,
+ num_workers=dataset_cfg.num_workers,
+ collate_fn=collator
+ )
+ if valid is not None:
+ self.valid_loader = DataLoader(
+ dataset=valid,
+ batch_size=dataset_cfg.batch_size_eval,
+ shuffle=False,
+ num_workers=dataset_cfg.num_workers,
+ collate_fn=collator,
+ )
+ self.test_loader = DataLoader(
+ dataset=test,
+ batch_size=dataset_cfg.batch_size_eval,
+ shuffle=False,
+ num_workers=dataset_cfg.num_workers,
+ collate_fn=collator,
+ )
+
+ def train_dataloader(self) -> TRAIN_DATALOADERS:
+ return self.train_loader
+
+ def val_dataloader(self) -> EVAL_DATALOADERS:
+ return self.valid_loader
+
+ def test_dataloader(self) -> EVAL_DATALOADERS:
+ return self.test_loader
+
+class DefaultModelWrapper(ModelWrapper):
+ def __init__(self, task: str, model_cfg: Config, train_cfg: Config) -> None:
+ super().__init__()
+ self.model_cfg = model_cfg
+ self.train_cfg = train_cfg
+ if getattr(model_cfg, 'pretrained', ''):
+ self.model = MODEL_REGISTRY[task][model_cfg.name].from_pretrained(model_cfg)
+ else:
+ self.model = MODEL_REGISTRY[task][model_cfg.name](model_cfg)
+ self.model.configure_task(task)
+
+ def get_featurizer(self) -> Tuple[Featurizer, Collator]:
+ return self.model.get_featurizer()
+
+ def forward(self, batch: Dict[str, Any]):
+ return self.model(**batch)
+
+ def predict(self, batch: Dict[str, Any]):
+ return self.model.predict(**batch)
+
+ def training_step(self, batch: Dict[str, Any], batch_idx: int) -> STEP_OUTPUT:
+ loss_dict = self.model(**batch)
+ loss_dict = {f"train/{k}": v for k, v in loss_dict.items()}
+ self.log_dict(loss_dict, batch_size=self.train_cfg.batch_size, sync_dist=True, prog_bar=True, logger=True)
+ return loss_dict["train/loss"]
+
+ def validation_step(self, batch: Dict[str, Any], batch_idx: int, *args, **kwargs) -> Optional[STEP_OUTPUT]:
+ return self.model.predict(**batch)
+
+ def test_step(self, batch: Dict[str, Any], batch_idx: int, *args, **kwargs) -> Optional[STEP_OUTPUT]:
+ return self.model.predict(**batch)
+
+ def configure_optimizers(self) -> Any:
+ if self.train_cfg.optimizer.name == "adam":
+ self.optimizer = torch.optim.Adam(
+ self.model.parameters(),
+ lr=self.train_cfg.optimizer.lr,
+ weight_decay=self.train_cfg.optimizer.weight_decay,
+ betas=(
+ getattr(self.train_cfg.optimizer, "beta1", 0.9),
+ getattr(self.train_cfg.optimizer, "beta2", 0.999),
+ )
+ )
+ else:
+ raise NotImplementedError(f'Optimizer not supported:{self.train_cfg.optimizer.name}')
+
+ if self.train_cfg.scheduler.name == "cosine":
+ scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+ self.optimizer,
+ T_max=self.train_cfg.max_iters,
+ eta_min=getattr(self.train_cfg.optimizer, "min_lr", self.train_cfg.optimizer.lr * 0.05),
+ )
+ self.scheduler = {
+ "scheduler": scheduler,
+ "interval": "step",
+ }
+ elif self.train_cfg.schedular == "plateau":
+ scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+ self.optimizer,
+ factor=getattr(self.train_cfg.scheduler, "factor", 0.6),
+ patience=getattr(self.train_cfg.scheduler, "patience", 10),
+ min_lr=getattr(self.train_cfg.scheduler, "min_lr", self.train_cfg.optimizer.lr * 0.05)
+ )
+ self.scheduler = {
+ "scheduler": scheduler,
+ "monitor": self.train_cfg.monitor.name,
+ "interval": "step",
+ "frequency": getattr(self.cfg, "val_freq", 1000)
+ }
+
+ return {
+ "optimizer": self.optimizer,
+ "lr_schedular": self.scheduler,
+ }
\ No newline at end of file
diff --git a/open_biomed/tasks/multi_modal_tasks/__init__.py b/open_biomed/tasks/multi_modal_tasks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/tasks/multi_modal_tasks/molecule_question_answering.py b/open_biomed/tasks/multi_modal_tasks/molecule_question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..7840224fffb4fa269162be213ffafa02a788ee0f
--- /dev/null
+++ b/open_biomed/tasks/multi_modal_tasks/molecule_question_answering.py
@@ -0,0 +1,69 @@
+from typing import Dict, List, Tuple, Optional
+from typing_extensions import Any
+
+import json
+import logging
+import os
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.callbacks import TextOverlapEvalCallback
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class MoleculeQA(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Molecule question answering.',
+ 'Inputs: {"molecule": a small molecule you are interested in. "text": a question about the molecule.}',
+ "Outputs: An answer to the question."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("molecule_question_answering", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("molecule_question_answering", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return MoleculeQAEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/ROUGE-1",
+ output_str="-BLEU-2_{val/BLEU-2:.4f}-BLEU-4_{val/BLEU-4:.4f}-ROUGE-1_{val/ROUGE-1:.4f}-ROUGE-2_{val/ROUGE-2:.4f}",
+ mode="max",
+ )
+
+class MoleculeQAEvaluationCallback(TextOverlapEvalCallback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = str(self.eval_dataset.labels[i])
+ logging.info(f"Molecule: {self.eval_dataset.molecules[i]}")
+ logging.info(f"Question: {self.eval_dataset.texts[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
\ No newline at end of file
diff --git a/open_biomed/tasks/multi_modal_tasks/molecule_text_translation.py b/open_biomed/tasks/multi_modal_tasks/molecule_text_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..90675609569855b53403c948b31bca597eadbdf0
--- /dev/null
+++ b/open_biomed/tasks/multi_modal_tasks/molecule_text_translation.py
@@ -0,0 +1,68 @@
+from typing import Dict, List, Tuple, Optional
+from typing_extensions import Any
+
+import json
+import logging
+import os
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import torch
+
+from open_biomed.data import molecule_fingerprint_similarity, check_identical_molecules
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.callbacks import TextOverlapEvalCallback
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class MoleculeCaptioning(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Molecule captioning.',
+ 'Inputs: {"molecule": a small molecule you are interested in.}',
+ "Outputs: Detailed textual descriptions of the molecule."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("molecule_captioning", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("molecule_captioning", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return MoleculeCaptioningEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/ROUGE-1",
+ output_str="-BLUE-2_{val/BLUE-2:.4f}-BLUE-4_{val/BLUE-4:.4f}-BLUE-L_{val/BLUE-L:.4f}-ROUGE-1_{val/ROUGE-1:.4f}-ROUGE-2_{val/ROUGE-2:.4f}",
+ mode="max",
+ )
+
+class MoleculeCaptioningEvaluationCallback(TextOverlapEvalCallback):
+ def __init__(self) -> None:
+ super().__init__(tokenizer_type="BERT")
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = str(self.eval_dataset.labels[i])
+ logging.info(f"Molecule: {self.eval_dataset.molecules[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
diff --git a/open_biomed/tasks/multi_modal_tasks/mutation_text_translation.py b/open_biomed/tasks/multi_modal_tasks/mutation_text_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..90c268b21e4ac9f958e23cdbe74f93487536304f
--- /dev/null
+++ b/open_biomed/tasks/multi_modal_tasks/mutation_text_translation.py
@@ -0,0 +1,100 @@
+from typing import Optional, Any
+
+import logging
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.callbacks import TextOverlapEvalCallback
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class MutationExplanation(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Mutation explanation.',
+ 'Inputs: {"protein": a protein (the amino acid sequence is required). "mutation": a string that describes a single-point substitution mutation. (e.g. A51F indicates that the 51st amino acid changes from A to F)}',
+ "Outputs: Textual descriptions of how this mutation affects protein function and further biological significance."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("mutation_explanation", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("mutation_explanation", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return MutationExplanationEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/ROUGE-1",
+ output_str="-BLUE-2_{val/BLUE-2:.4f}-BLUE-4_{val/BLUE-4:.4f}-BLUE-L_{val/BLUE-L:.4f}-ROUGE-1_{val/ROUGE-1:.4f}-ROUGE-2_{val/ROUGE-2:.4f}",
+ mode="max",
+ )
+
+class MutationExplanationEvaluationCallback(TextOverlapEvalCallback):
+ def __init__(self) -> None:
+ super().__init__(tokenizer_type=None)
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = str(self.eval_dataset.labels[i])
+ logging.info(f"Protein: {self.eval_dataset.protein[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
+
+class MutationEngineering(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Mutation engineering.',
+ 'Inputs: {"protein": a protein (the amino acid sequence is required). "prompt": A textual prompt indicating the desired property of the mutant.}',
+ "Outputs: A string that describes a single-point substitution mutation. (e.g. A51F indicates that the 51st amino acid changes from A to F)."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("mutation_engineering", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("mutation_engineering", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return MutationEngineeringEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/recall@50",
+ output_str="-Recall@50_{val/recall@50:.4f}-Accuracy_{val/accuracy:.4f}",
+ mode="max",
+ )
+
+# TODO: implement mutation engineering
+class MutationEngineeringEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
\ No newline at end of file
diff --git a/open_biomed/tasks/multi_modal_tasks/protein_question_answering.py b/open_biomed/tasks/multi_modal_tasks/protein_question_answering.py
new file mode 100644
index 0000000000000000000000000000000000000000..19665656dd7b46742e681854d25732f44571b514
--- /dev/null
+++ b/open_biomed/tasks/multi_modal_tasks/protein_question_answering.py
@@ -0,0 +1,70 @@
+from typing import Dict, List, Tuple, Optional
+from typing_extensions import Any
+
+import json
+import logging
+import os
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import torch
+
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.callbacks import TextOverlapEvalCallback
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class ProteinQA(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Protein question answering.',
+ 'Inputs: {"protein": a protein you are interested in. "text": a question about the protein.}',
+ "Outputs: An answer to the question."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("protein_question_answering", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("protein_question_answering", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return ProteinQAEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/ROUGE-1",
+ output_str="-BLUE-2_{val/BLEU-2:.4f}-BLUE-4_{val/BLEU-4:.4f}-ROUGE-1_{val/ROUGE-1:.4f}-ROUGE-2_{val/ROUGE-2:.4f}",
+ mode="max",
+ )
+
+class ProteinQAEvaluationCallback(TextOverlapEvalCallback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = str(self.eval_dataset.labels[i])
+ logging.info(f"Question: {self.eval_dataset.texts[i]}")
+ logging.info(f"Protein: {self.eval_dataset.proteins[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
\ No newline at end of file
diff --git a/open_biomed/tasks/multi_modal_tasks/protein_text_translation.py b/open_biomed/tasks/multi_modal_tasks/protein_text_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..18ee43f12265589d87ddb5246aa7401805a136e7
--- /dev/null
+++ b/open_biomed/tasks/multi_modal_tasks/protein_text_translation.py
@@ -0,0 +1,114 @@
+from typing import Optional
+from typing_extensions import Any
+
+import json
+import logging
+import numpy as np
+import os
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import re
+
+from open_biomed.data import protein_sequence_similarity
+from open_biomed.utils.callbacks import TextOverlapEvalCallback
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class TextBasedProteinGeneration(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Text-based protein generation.',
+ 'Inputs: {"text": Textual instructions descrbing the desired properties of the designed protein.}',
+ "Outputs: A protein sequence or structure."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("text_based_protein_generation", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("text_based_protein_generation", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return TextBasedProteinGenerationEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/similarity",
+ output_str="-similarity_{val/similarity:.4f}",
+ mode="max",
+ )
+
+class TextBasedProteinGenerationEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = str(self.eval_dataset.labels[i])
+ logging.info(f"Text: {self.eval_dataset.texts[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+ output_path = os.path.join(trainer.default_root_dir, f"{self.status}_outputs", f"epoch{pl_module.current_epoch}")
+ scores = []
+ with open(output_path + "_outputs.txt", "w") as f:
+ f.write("Text\tPredicted\tGround Truth\tScore\n")
+ for i in range(len(self.outputs)):
+ score, seq1, seq2 = protein_sequence_similarity(self.outputs[i], self.eval_dataset.proteins[i])
+ scores.append(score)
+ text = re.sub(r"[\n\t]", "", self.eval_dataset.texts[i].str)
+ f.write(f"{text}\n{seq1}\n{seq2}\n{score:.4f}\n")
+ out_metrics = {f"{self.status}/similarity": np.mean(scores)}
+ pl_module.log_dict(out_metrics)
+ json.dump(out_metrics, open(output_path + "_metrics.json", "w"))
+
+ def on_test_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0,
+ ) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_test_epoch_start(trainer, pl_module)
+ self.status = "test"
+ self.outputs = []
+ self.eval_dataset = trainer.test_dataloaders.dataset
+
+ def on_test_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
\ No newline at end of file
diff --git a/open_biomed/tasks/multi_modal_tasks/text_based_molecule_editing.py b/open_biomed/tasks/multi_modal_tasks/text_based_molecule_editing.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3b085c44ca57f5df0eebcdaa9490e7291bffbb0
--- /dev/null
+++ b/open_biomed/tasks/multi_modal_tasks/text_based_molecule_editing.py
@@ -0,0 +1,143 @@
+from typing import Dict, List, Tuple, Optional
+from typing_extensions import Any
+
+import json
+import logging
+import os
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import torch
+
+from open_biomed.data import molecule_fingerprint_similarity, check_identical_molecules
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class TextMoleculeEditing(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Text-based molecule editing.',
+ 'Inputs: {"molecule": a small molecule, "text": the desired property of the updated molecule}',
+ "Outputs: A new molecule that is structurally similar to the original molecule but exhibit improved property described in text."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("text_based_molecule_editing", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("text_based_molecule_editing", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return TextMoleculeEditingEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/ratio_improved",
+ output_str="-validity_{val/validity:.4f}-ratio_improved_{val/ratio_improved:.4f}-accuracy_{val/accuracy:.4f}",
+ mode="max",
+ )
+
+class TextMoleculeEditingEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = '\n'.join([str(x) for x in self.eval_dataset.labels[i]])
+ logging.info(f"Original: {self.eval_dataset.molecules[i]}")
+ logging.info(f"Prompt: {self.eval_dataset.texts[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ cnt_valid, cnt_improved, cnt_accurate = 0, 0, 0
+ output_str = "\t".join(["Original", "Prompt", "Predicted", "Best_Reference", "All_Reference", "FP_Similarity", "FP_Similarity_orig"]) + "\n"
+ for i in range(len(self.outputs)):
+ best_sim, best_sim_orig = 0.0, 0.0
+ best_ref = None
+
+ orig_mol, prompt, label = self.eval_dataset.molecules[i], self.eval_dataset.texts[i], self.eval_dataset.labels[i]
+ all_ref = []
+ for ref_mol in label:
+ cur_sim = molecule_fingerprint_similarity(self.outputs[i], ref_mol)
+ if cur_sim > best_sim:
+ best_sim = cur_sim
+ best_ref = ref_mol
+ all_ref.append(str(ref_mol))
+ cur_sim = molecule_fingerprint_similarity(orig_mol, ref_mol)
+ if cur_sim > best_sim_orig:
+ best_sim_orig = cur_sim
+
+ output_str += "\t".join([str(orig_mol), str(prompt), str(self.outputs[i]), str(best_ref), ",".join(all_ref), f"{best_sim:.4f}", f"{best_sim_orig:.4f}"]) + "\n"
+ if self.outputs[i].rdmol is not None:
+ cnt_valid += 1
+ if best_sim > best_sim_orig:
+ cnt_improved += 1
+ if check_identical_molecules(self.outputs[i], best_ref):
+ cnt_accurate += 1
+
+ output_path = os.path.join(trainer.default_root_dir, f"{self.status}_outputs", f"epoch{pl_module.current_epoch}")
+ out_metrics = {
+ f"{self.status}/validity": cnt_valid / len(self.outputs),
+ f"{self.status}/ratio_improved": cnt_improved / len(self.outputs),
+ f"{self.status}/accuracy": cnt_accurate / len(self.outputs),
+ }
+ pl_module.log_dict(out_metrics)
+ print(json.dumps(out_metrics, indent=4))
+ json.dump(out_metrics, open(output_path + "_metrics.json", "w"))
+
+ with open(output_path + "_outputs.txt", "w") as f:
+ f.write(output_str)
+
+
+ def on_test_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0,
+ ) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_test_epoch_start(trainer, pl_module)
+ self.status = "test"
+ self.outputs = []
+ self.eval_dataset = trainer.test_dataloaders.dataset
+
+ def on_test_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
\ No newline at end of file
diff --git a/open_biomed/tasks/multi_modal_tasks/text_guided_molecule_generation.py b/open_biomed/tasks/multi_modal_tasks/text_guided_molecule_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d58d1312dda56fda079106c41387f9356643572
--- /dev/null
+++ b/open_biomed/tasks/multi_modal_tasks/text_guided_molecule_generation.py
@@ -0,0 +1,129 @@
+from typing import Dict, List, Tuple, Optional
+from typing_extensions import Any
+
+import json
+import logging
+import os
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import torch
+
+from open_biomed.data import molecule_fingerprint_similarity, check_identical_molecules
+from open_biomed.tasks.base_task import BaseTask, DefaultDataModule, DefaultModelWrapper
+from open_biomed.utils.collator import Collator
+from open_biomed.utils.config import Config, Struct
+from open_biomed.utils.featurizer import Featurizer
+
+class TextGuidedMoleculeGeneration(BaseTask):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @staticmethod
+ def print_usage() -> str:
+ return "\n".join([
+ 'Text-based molecule generation.',
+ 'Inputs: {"text": textual descriptions of the desired molecule.}',
+ "Outputs: A new molecule that best fits the textual instruction."
+ ])
+
+ @staticmethod
+ def get_datamodule(dataset_cfg: Config, featurizer: Featurizer, collator: Collator) -> pl.LightningDataModule:
+ return DefaultDataModule("text_guided_molecule_generation", dataset_cfg, featurizer, collator)
+
+ @staticmethod
+ def get_model_wrapper(model_cfg: Config, train_cfg: Config) -> pl.LightningModule:
+ return DefaultModelWrapper("text_guided_molecule_generation", model_cfg, train_cfg)
+
+ @staticmethod
+ def get_callbacks(callback_cfg: Optional[Config]=None) -> pl.Callback:
+ return TextGuidedMoleculeGenerationEvaluationCallback()
+
+ @staticmethod
+ def get_monitor_cfg() -> Struct:
+ return Struct(
+ name="val/accuracy",
+ output_str="-accuracy_{val/accuracy:.4f}-validity_{val/validity:.4f}",
+ mode="max",
+ )
+
+class TextGuidedMoleculeGenerationEvaluationCallback(pl.Callback):
+ def __init__(self) -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+ if batch_idx == 0:
+ for i in range(2):
+ out_labels = str(self.eval_dataset.labels[i])
+ logging.info(f"Original: {self.eval_dataset.texts[i]}")
+ logging.info(f"Predict: {self.outputs[i]}")
+ logging.info(f"Ground Truth: {out_labels}")
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ gts = self.eval_dataset.labels
+
+ cnt_valid, cnt_accurate = 0, 0
+ output_str = ""
+ for i in range(len(self.outputs)):
+ text, label = self.eval_dataset.texts[i], self.eval_dataset.labels[i]
+ cur_sim = molecule_fingerprint_similarity(self.outputs[i], gts[i])
+
+ output_str += "\t".join([str(text), str(label), str(self.outputs[i]), f"{cur_sim:.4f}"]) + "\n"
+ if self.outputs[i].rdmol is not None:
+ cnt_valid += 1
+ if check_identical_molecules(self.outputs[i], gts[i]):
+ cnt_accurate += 1
+
+ output_path = os.path.join(trainer.default_root_dir, f"{self.status}_outputs", f"epoch{pl_module.current_epoch}")
+ out_metrics = {
+ f"{self.status}/validity": cnt_valid / len(self.outputs),
+ f"{self.status}/accuracy": cnt_accurate / len(self.outputs),
+ }
+ pl_module.log_dict(out_metrics)
+ print(json.dumps(out_metrics, indent=4))
+ json.dump(out_metrics, open(output_path + "_metrics.json", "w"))
+
+ with open(output_path + "_outputs.txt", "w") as f:
+ f.write(output_str)
+
+
+ def on_test_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0,
+ ) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_test_epoch_start(trainer, pl_module)
+ self.status = "test"
+ self.outputs = []
+ self.eval_dataset = trainer.test_dataloaders.dataset
+
+ def on_test_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
\ No newline at end of file
diff --git a/open_biomed/tasks/multi_modal_tasks/vision_to_molecule.py b/open_biomed/tasks/multi_modal_tasks/vision_to_molecule.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/utils/__init__.py b/open_biomed/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/utils/callbacks.py b/open_biomed/utils/callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6e1bcb617ca28274a8687e7c04df74adf080e93
--- /dev/null
+++ b/open_biomed/utils/callbacks.py
@@ -0,0 +1,235 @@
+from typing import Optional, Union
+from typing_extensions import Any
+
+from absl import logging
+import json
+import numpy as np
+import os
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+import re
+import torch
+
+from transformers import BertTokenizerFast
+
+class Queue:
+ def __init__(self, max_len=50):
+ self.items = [1]
+ self.max_len = max_len
+
+ def __len__(self):
+ return len(self.items)
+
+ def add(self, item):
+ self.items.insert(0, item)
+ if len(self) > self.max_len:
+ self.items.pop()
+
+ def mean(self):
+ return np.mean(self.items)
+
+ def std(self):
+ return np.std(self.items)
+
+class GradientClip(pl.Callback):
+ def __init__(self, max_grad_norm: Union[float, str]='Q', Q=Queue(3000)) -> None:
+ super().__init__()
+ # self.max_norm = max_norm
+ self.gradnorm_queue = Q
+ if max_grad_norm == 'Q':
+ self.max_grad_norm = max_grad_norm
+ else:
+ self.max_grad_norm = float(max_grad_norm)
+
+ def on_before_optimizer_step(self, trainer: pl.Trainer, pl_module: pl.LightningModule, optimizer: torch.optim.Optimizer) -> None:
+ # zero graidents if they are not finite
+ if self.max_grad_norm == 'Q':
+ max_grad_norm = 1.5 * self.gradnorm_queue.mean() + 2 * self.gradnorm_queue.std()
+ max_grad_norm = max_grad_norm.item()
+ else:
+ max_grad_norm = self.max_grad_norm
+ grad_norm = torch.nn.utils.clip_grad_norm_(
+ pl_module.parameters(), max_norm=max_grad_norm, norm_type=2.0
+ )
+
+ if self.max_grad_norm == 'Q':
+ if float(grad_norm) > max_grad_norm:
+ self.gradnorm_queue.add(float(max_grad_norm))
+ else:
+ self.gradnorm_queue.add(float(grad_norm))
+
+ if float(grad_norm) > max_grad_norm:
+ logging.info(
+ f"Clipped gradient with value {grad_norm:.1f} "
+ f"while allowed {max_grad_norm:.1f}",
+ )
+ pl_module.log_dict(
+ {
+ "grad_norm": grad_norm.item(),
+ 'max_grad_norm': max_grad_norm,
+ },
+ on_step=True,
+ prog_bar=False,
+ logger=True,
+ batch_size=pl_module.train_cfg.batch_size,
+ )
+
+class RecoverCallback(pl.Callback):
+ def __init__(self, latest_ckpt: str, recover_trigger_loss: float=1e3, resume: bool=False) -> None:
+ super().__init__()
+ self.latest_ckpt = latest_ckpt
+ self.recover_trigger_loss = recover_trigger_loss
+ self.resume = resume
+
+ def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: str) -> None:
+ super().setup(trainer, pl_module, stage)
+ if os.path.exists(self.latest_ckpt) and self.resume:
+ print(f"recover from checkpoint: {self.latest_ckpt}")
+ checkpoint = torch.load(self.latest_ckpt)
+ pl_module.load_state_dict(checkpoint["state_dict"])
+ # pl_module.load_from_checkpoint(self.latest_ckpt)
+ elif not os.path.exists(self.latest_ckpt) and self.resume:
+ print(
+ f"checkpoint {self.latest_ckpt} not found, training from scratch"
+ )
+
+ def on_train_batch_end(
+ self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ ) -> None:
+ super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx)
+ if "loss" not in outputs:
+ return None
+
+ if outputs["loss"] > self.recover_trigger_loss:
+ logging.warning(
+ f"loss too large: {outputs}\n recovering from checkpoint: {self.latest_ckpt}"
+ )
+ if os.path.exists(self.latest_ckpt):
+ checkpoint = torch.load(self.latest_ckpt)
+ pl_module.load_state_dict(checkpoint["state_dict"])
+ else:
+ for layer in pl_module.children():
+ if hasattr(layer, "reset_parameters"):
+ layer.reset_parameters()
+ logging.warning(
+ f"checkpoint {self.latest_ckpt} not found, training from scratch"
+ )
+
+ else:
+ pass
+
+class TextOverlapEvalCallback(pl.Callback):
+ def __init__(self, tokenizer_type: Optional[str]="BERT") -> None:
+ super().__init__()
+ self.outputs = []
+ self.eval_dataset = None
+ self.tokenizer_type = tokenizer_type
+ if tokenizer_type == "BERT":
+ abs_path = os.path.abspath("./checkpoints/tokenizers/bert-base-uncased/")
+ self.tokenizer = BertTokenizerFast.from_pretrained(abs_path)
+ self.filter_tokens = ["[PAD]", "[CLS]", "[SEP]"]
+ if tokenizer_type == "every" or tokenizer_type is None:
+ self.tokenizer = None
+ self.filter_tokens = []
+
+ def on_validation_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: Optional[STEP_OUTPUT],
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0
+ ) -> None:
+ super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+ self.outputs.extend(outputs)
+
+ def on_validation_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_validation_epoch_start(trainer, pl_module)
+ self.status = "val"
+ self.outputs = []
+ self.eval_dataset = trainer.val_dataloaders.dataset
+
+ def on_validation_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+ from nltk.translate.meteor_score import meteor_score
+ from rouge_score import rouge_scorer
+
+ scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'])
+
+ gts = self.eval_dataset.labels
+ meteor_scores = []
+ rouge_scores_1 = []
+ rouge_scores_2 = []
+ rouge_scores_L = []
+ bleu2, bleu4 = [], []
+ for i in range(len(self.outputs)):
+ if self.tokenizer_type == "every":
+ self.outputs[i] = " ".join([x for x in str(self.outputs[i])])
+ gts[i] = " ".join([x for x in str(gts[i])])
+ rouge = scorer.score(str(self.outputs[i]), str(gts[i]))
+ rouge_scores_1.append(rouge['rouge1'].fmeasure)
+ rouge_scores_2.append(rouge['rouge2'].fmeasure)
+ rouge_scores_L.append(rouge['rougeL'].fmeasure)
+
+ if self.tokenizer_type == "BERT":
+ output_token = self.tokenizer.tokenize(str(self.outputs[i]), truncation=True, max_length=512, padding='max_length')
+ gt_token = self.tokenizer.tokenize(str(gts[i]), truncation=True, max_length=512, padding='max_length')
+ elif self.tokenizer_type == "every" or self.tokenizer_type is None:
+ output_token = str(self.outputs[i]).split(" ")
+ gt_token = str(gts[i]).split(" ")
+ for token in self.filter_tokens:
+ output_token = list(filter(token.__ne__, output_token))
+ gt_token = list(filter(token.__ne__, gt_token))
+ bleu2.append(sentence_bleu([gt_token], output_token, weights=(0.5, 0.5), smoothing_function=SmoothingFunction().method1))
+ bleu4.append(sentence_bleu([gt_token], output_token, weights=(0.25, 0.25, 0.25, 0.25), smoothing_function=SmoothingFunction().method1))
+ meteor_scores.append(meteor_score([gt_token], output_token))
+
+ output_path = os.path.join(trainer.default_root_dir, f"{self.status}_outputs", f"epoch{pl_module.current_epoch}")
+ out_metrics = {
+ f"{self.status}/BLEU-2": np.mean(bleu2),
+ f"{self.status}/BLEU-4": np.mean(bleu4),
+ f"{self.status}/METEOR": np.mean(meteor_scores),
+ f"{self.status}/ROUGE-1": np.mean(rouge_scores_1),
+ f"{self.status}/ROUGE-2": np.mean(rouge_scores_2),
+ f"{self.status}/ROUGE-L": np.mean(rouge_scores_L)
+ }
+ pl_module.log_dict(out_metrics)
+ print(json.dumps(out_metrics, indent=4))
+ json.dump(out_metrics, open(output_path + "_metrics.json", "w"))
+
+ with open(output_path + "_outputs.txt", "w") as f:
+ f.write("Predicted\tGround Truth\tBLEU-2\tBLEU-4\tROUGE-1\tROUGE-2\tROUGE-L\n")
+ for i in range(len(self.outputs)):
+ self.outputs[i] = re.sub(r"[\n\t]", "", str(self.outputs[i]))
+ gts[i] = re.sub(r"[\n\t]", "", str(gts[i]))
+ f.write(f"{self.outputs[i]}\t{gts[i]}\t{bleu2[i]:.4f}\t{bleu4[i]:.4f}\t{rouge_scores_1[i]:.4f}\t{rouge_scores_2[i]:.4f}\t{rouge_scores_L[i]:.4f}\n")
+
+ def on_test_batch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule,
+ outputs: STEP_OUTPUT,
+ batch: Any,
+ batch_idx: int,
+ dataloader_idx: int = 0,
+ ) -> None:
+ self.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+
+ def on_test_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+ super().on_test_epoch_start(trainer, pl_module)
+ self.status = "test"
+ self.outputs = []
+ self.eval_dataset = trainer.test_dataloaders.dataset
+
+ def on_test_epoch_end(self,
+ trainer: pl.Trainer,
+ pl_module: pl.LightningModule
+ ) -> None:
+ self.on_validation_epoch_end(trainer, pl_module)
\ No newline at end of file
diff --git a/open_biomed/utils/cluster.py b/open_biomed/utils/cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..91275db16d8002a508ffa8ecbf9248226dab6d19
--- /dev/null
+++ b/open_biomed/utils/cluster.py
@@ -0,0 +1,48 @@
+import logging
+logger = logging.getLogger(__name__)
+import numpy as np
+
+class UFS(object):
+ def __init__(self, n):
+ self.fa = list(range(n))
+
+ def merge(self, x, y):
+ self.fa[x] = self.find(y)
+
+ def find(self, x):
+ self.fa[x] = self.find(self.fa[x]) if self.fa[x] != x else x
+ return self.fa[x]
+
+def cluster_with_sim_matrix(sim_matrix, threshold):
+ n = len(sim_matrix)
+ e = []
+ f = UFS(n)
+ for i in range(n):
+ for j in range(n):
+ x, y = f.find(i), f.find(j)
+ if x != y and sim_matrix[x][y] > threshold:
+ f.merge(x, y)
+ for k in range(n):
+ sim_matrix[y][k] = min(sim_matrix[y][k], sim_matrix[x][k])
+ clusters = [[] for i in range(n)]
+ for i in range(n):
+ clusters[f.find(i)].append(i)
+ return clusters
+
+def merge_cluster(clusters, n_cluster):
+ merged_clusters = [[] for i in range(n_cluster)]
+ n_cutoff = np.sum([len(cluster) for cluster in clusters]) // n_cluster
+ perm = np.random.permutation(len(clusters))
+ cur = 0
+ for i in perm:
+ if cur < n_cluster - 1 and len(merged_clusters[cur]) + len(clusters[i]) > n_cutoff:
+ if len(merged_clusters[cur]) + len(clusters[i]) - n_cutoff > n_cutoff - len(merged_clusters[cur]):
+ cur += 1
+ merged_clusters[cur].extend(clusters[i])
+ else:
+ merged_clusters[cur].extend(clusters[i])
+ cur += 1
+ else:
+ merged_clusters[cur].extend(clusters[i])
+ logger.info("cluster size: %s" % (", ".join([str(len(merged_cluster)) for merged_cluster in merged_clusters])))
+ return merged_clusters
\ No newline at end of file
diff --git a/open_biomed/utils/collator.py b/open_biomed/utils/collator.py
new file mode 100644
index 0000000000000000000000000000000000000000..b76740bc119a0ccdfa86eeb8ea77945e25f85d6d
--- /dev/null
+++ b/open_biomed/utils/collator.py
@@ -0,0 +1,66 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+from torch_geometric.data import Data, Batch
+import torch
+
+from transformers import AutoTokenizer, DataCollatorWithPadding
+
+class Collator(ABC):
+ def __init__(self) -> None:
+ pass
+
+ @abstractmethod
+ def __call__(self, inputs: List[Any]) -> Any:
+ raise NotImplementedError
+
+ def _collate_single(self, data):
+ if isinstance(data[0], Data):
+ return Batch.from_data_list(data)
+ elif torch.is_tensor(data[0]):
+ return torch.stack([x.squeeze() for x in data])
+ elif isinstance(data[0], int):
+ return torch.tensor(data).view((-1, 1))
+
+class PygCollator(Collator):
+ def __init__(self, follow_batch: List[str]=[], exclude_keys: List[str]=[]) -> None:
+ super().__init__()
+ self.follow_batch = follow_batch
+ self.exclude_keys = exclude_keys
+
+ def __call__(self, inputs: List[Data]) -> Batch:
+ return Batch.from_data_list(inputs, follow_batch=self.follow_batch, exclude_keys=self.exclude_keys)
+
+class ListCollator(Collator):
+ def __call__(self, inputs: List[Any]) -> Any:
+ return inputs
+
+class ClassLabelCollator(Collator):
+ def __call__(self, inputs: List[Any]) -> Any:
+ batch = torch.stack(inputs)
+ return batch
+
+
+class DPCollator(Collator):
+ def __init__(self):
+ super(DPCollator, self).__init__()
+
+ def __call__(self, mols):
+ batch = self._collate_single(mols)
+ return batch
+
+
+class EnsembleCollator(Collator):
+ def __init__(self, to_ensemble: Dict[str, Collator]) -> None:
+ super().__init__()
+ self.collators = {}
+ for k, v in to_ensemble.items():
+ self.collators[k] = v
+
+ def __call__(self, inputs: List[Dict[str, Any]]) -> Dict[Any, Any]:
+ collated = {}
+ for k in inputs[0]:
+ collated[k] = self.collators[k]([item[k] for item in inputs])
+ return collated
+
+ def get_attrs(self) -> List[str]:
+ return list(self.collators.keys())
\ No newline at end of file
diff --git a/open_biomed/utils/config.py b/open_biomed/utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..6174e32a5086dc2e1c877d32759bb5fd1dd9ed07
--- /dev/null
+++ b/open_biomed/utils/config.py
@@ -0,0 +1,265 @@
+from typing import Dict
+
+import yaml
+import os
+import re
+from ast import literal_eval
+import argparse
+import json
+import copy
+
+class Struct:
+ def __init__(self, **kwargs):
+ for key, value in kwargs.items():
+ if isinstance(value, dict):
+ self.__dict__[key] = Struct(**value)
+ else:
+ self.__dict__[key] = value
+
+ def todict(self):
+ # recursively convert to dict
+ return {
+ k: v.todict() if isinstance(v, Struct) else v
+ for k, v in self.__dict__.items()
+ }
+
+ def __getitem__(self, index):
+ return self.__dict__[index]
+
+
+class Config:
+ def __init__(self, config_file=None, **kwargs):
+ if config_file is not None:
+ _config = parse_config(path=config_file, subs_dict=kwargs)
+ else:
+ _config = {}
+ for key, value in _config.items():
+ if isinstance(value, dict):
+ self.__dict__[key] = Struct(**value)
+ else:
+ self.__dict__[key] = value
+
+ @classmethod
+ def from_dict(cls, **kwargs):
+ cfg = cls(None)
+ for key, value in kwargs.items():
+ if isinstance(value, dict):
+ cfg.__dict__[key] = Struct(**value)
+ else:
+ cfg.__dict__[key] = value
+ return cfg
+
+ def __getitem__(self, index):
+ return self.__dict__[index]
+
+ def todict(self):
+ # recursively convert to dict
+ return {
+ k: v.todict() if isinstance(v, Struct) else v
+ for k, v in self.__dict__.items()
+ }
+
+ def save2yaml(self, path):
+ with open(path, "w") as f:
+ yaml.dump(self.todict(), f, default_flow_style=False)
+
+ def __str__(self):
+ def prepare_dict4print(dict_):
+ tmp_dict = copy.deepcopy(dict_)
+
+ def recursive_change_list_to_string(d, summarize=16):
+ for k, v in d.items():
+ if isinstance(v, dict):
+ recursive_change_list_to_string(v)
+ elif isinstance(v, list):
+ d[k] = (
+ (
+ str(
+ v[: summarize // 2] + ["..."] + v[-summarize // 2 :]
+ )
+ + f" (len={len(v)})"
+ )
+ if len(v) > summarize
+ else str(v) + f" (len={len(v)})"
+ )
+ else:
+ pass
+
+ recursive_change_list_to_string(tmp_dict)
+ return tmp_dict
+
+ return json.dumps(prepare_dict4print(self.todict()), indent=4, sort_keys=False)
+
+
+def simplest_type(s):
+ try:
+ return literal_eval(s)
+ except:
+ return s
+
+
+# enable parsing of environment variables in yaml files
+def parse_config(
+ path=None,
+ data=None,
+ subs_dict={},
+ envtag="!ENV",
+ substag="!SUB",
+ envsubstag="!ENVSUB",
+):
+ # pattern for global vars: look for ${word}
+ pattern = re.compile(".*?\${([\w:\-\.]+)}.*?")
+ loader = yaml.FullLoader
+
+ # the tag will be used to mark where to start searching for the pattern
+ # e.g. somekey: !ENV somestring${MYENVVAR}blah blah blah
+ loader.add_implicit_resolver(envtag, pattern, None)
+ loader.add_implicit_resolver(substag, pattern, None)
+ loader.add_implicit_resolver(envsubstag, pattern, None)
+
+ def constructor_env_variables(loader, node):
+ """
+ Extracts the environment variable from the node's value
+ :param yaml.Loader loader: the yaml loader
+ :param node: the current node in the yaml
+ :return: the parsed string that contains the value of the environment
+ variable
+ """
+ value = loader.construct_scalar(node)
+ match = pattern.findall(value) # to find all env variables in line
+ if match:
+ full_value = value
+ for g in match:
+ vname, sep, default_val = g.partition(":-")
+ full_value = full_value.replace(
+ f"${{{g}}}", os.environ.get(vname, default_val)
+ )
+ return simplest_type(full_value)
+ return simplest_type(value)
+
+ def constructor_subs_variables(loader, node):
+ """
+ Extracts the environment variable from the node's value
+ :param yaml.Loader loader: the yaml loader
+ :param node: the current node in the yaml
+ :return: the parsed string that contains the value of the environment
+ variable
+ """
+ value = loader.construct_scalar(node)
+ match = pattern.findall(value) # to find all env variables in line
+ # print(value)
+ # print(match)
+ if match:
+ full_value = value
+ for g in match:
+ vname, sep, default_val = g.partition(":-")
+ if len(str(subs_dict.get(vname, default_val))) == 0:
+ raise ValueError(
+ f"""argument `{vname}` required,
+ should be specified from command line with: --{vname} ,
+ or set a default value for `{vname}` in `{path}` file.
+ """
+ )
+ full_value = full_value.replace(
+ f"${{{g}}}", str(subs_dict.get(vname, default_val))
+ )
+ return simplest_type(full_value)
+ return simplest_type(value)
+
+ def constructor_envsubs_variables(loader, node):
+ """
+ Extracts the environment variable from the node's value
+ :param yaml.Loader loader: the yaml loader
+ :param node: the current node in the yaml
+ :return: the parsed string that contains the value of the environment
+ variable
+ """
+ value = loader.construct_scalar(node)
+ match = pattern.findall(value) # to find all env variables in line
+ if match:
+ full_value = value
+ for g in match:
+ vname, sep, default_val = g.partition(":-")
+ if (
+ len(str(subs_dict.get(vname, default_val))) == 0
+ and len(str(os.environ.get(vname, default_val))) == 0
+ ):
+ raise ValueError(
+ f"""argument `{vname}` required,
+ should be specified from command line with: --{vname} ,
+ or set a default value for `{vname}` in `{path}` file.
+ """
+ )
+ full_value = full_value.replace(
+ f"${{{g}}}",
+ str(subs_dict.get(vname, os.environ.get(vname, default_val))),
+ )
+ return simplest_type(full_value)
+ return simplest_type(value)
+
+ def constructor_pathjoin(loader, node):
+ value = loader.construct_sequence(node)
+ value = [str(v) for v in value]
+ return os.path.join(*value)
+
+ def constructor_strjoin(loader, node):
+ value = loader.construct_sequence(node)
+ value = [str(v) for v in value]
+ return "".join(value)
+
+ def constructor_listadd(loader, node):
+ value = loader.construct_sequence(node)
+ value = sum([simplest_type(v) for v in value])
+ return value
+
+ def constructor_listmul(loader, node):
+ value = loader.construct_sequence(node)
+ ret = 1
+ for v in value:
+ ret *= simplest_type(v)
+ return ret
+
+ loader.add_constructor(envtag, constructor_env_variables)
+ loader.add_constructor(substag, constructor_subs_variables)
+ loader.add_constructor(envsubstag, constructor_envsubs_variables)
+ loader.add_constructor("!PATHJOIN", constructor_pathjoin)
+ loader.add_constructor("!STRJOIN", constructor_strjoin)
+ loader.add_constructor("!LISTADD", constructor_listadd)
+ loader.add_constructor("!LISTMUL", constructor_listmul)
+
+ if path:
+ with open(path) as conf_data:
+ return yaml.load(conf_data, Loader=loader)
+ elif data:
+ return yaml.load(data, Loader=loader)
+ else:
+ raise ValueError("Either a path or data should be defined as input")
+
+def merge_config(cfg1: Config, cfg2: Config) -> Config:
+ # Combine config values in cfg2 into cfg
+ # NOTE: the values in cfg2 will overwrite those in cfg1 for overlapping keys
+ def recursive_merge(cfg1: Struct, cfg2: Struct) -> None:
+ for key, value in cfg2.__dict__.items():
+ if isinstance(value, Struct) and key in cfg1.__dict__:
+ recursive_merge(cfg1.__dict__[key], cfg2.__dict__[key])
+ else:
+ cfg1.__dict__[key] = value
+ return cfg1
+ return recursive_merge(cfg1, cfg2)
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--config_file",
+ type=str,
+ default="../debug.yaml",
+ )
+ parser.add_argument("--batch_size", type=int, default=32)
+ parser.add_argument("--no_wandb", action="store_true")
+ parser.add_argument("--lr", type=float, default=1e-3)
+ parser.add_argument("--exp_name", type=str, default="debug")
+ parser.add_argument("--debug", action="store_true")
+ parser.add_argument("--epochs", type=int, default=100)
+ _args, unknown = parser.parse_known_args()
+ cfg = Config(**_args.__dict__)
+ print(f"The config of this process is:\n{cfg}")
diff --git a/open_biomed/utils/distributed.py b/open_biomed/utils/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..f16a97a8d4f6481561d13e9259bad6f2db7792d9
--- /dev/null
+++ b/open_biomed/utils/distributed.py
@@ -0,0 +1,14 @@
+def setup_outputs_for_distributed(is_master, cfg):
+ """
+ This function disables printing when not in master process
+ """
+ import builtins as __builtin__
+ builtin_print = __builtin__.print
+
+ def print(*args, **kwargs):
+ force = kwargs.pop('force', False)
+ if is_master or force:
+ builtin_print(*args, **kwargs)
+
+ __builtin__.print = print
+ cfg.logging_level = "error"
\ No newline at end of file
diff --git a/open_biomed/utils/exception.py b/open_biomed/utils/exception.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bd1ab5836945464da902d844a5b1a5c1e1123d4
--- /dev/null
+++ b/open_biomed/utils/exception.py
@@ -0,0 +1,9 @@
+
+
+class MoleculeConstructError(Exception):
+ def __init__(self, *args: object) -> None:
+ super(MoleculeConstructError, self).__init__(*args)
+
+class ProteinConstructError(Exception):
+ def __init__(self, *args: object) -> None:
+ super(ProteinConstructError).__init__(*args)
\ No newline at end of file
diff --git a/open_biomed/utils/featurizer.py b/open_biomed/utils/featurizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..65c76a0f28d303c45b1d5dcb9d7a6225c6b17e9b
--- /dev/null
+++ b/open_biomed/utils/featurizer.py
@@ -0,0 +1,179 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Generic, List, TypeVar
+
+import os
+import sys
+import numpy as np
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+import torch
+from transformers import BatchEncoding, T5Tokenizer
+from transformers.tokenization_utils import PreTrainedTokenizer
+
+from open_biomed.data import Molecule, Protein, Pocket, Text
+
+T = TypeVar('T', bound=Any)
+
+class Featurized(Generic[T]):
+ def __init__(self, value: T):
+ self.value = value
+
+class Featurizer(ABC):
+ def __init__(self) -> None:
+ pass
+
+ @abstractmethod
+ def __call__(self, *args: Any, **kwargs: Any) -> Dict[str, Any]:
+ raise NotImplementedError
+
+ @abstractmethod
+ def get_attrs(self) -> List[str]:
+ raise NotImplementedError
+
+class MoleculeFeaturizer(Featurizer):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @abstractmethod
+ def __call__(self, molecule: Molecule) -> Dict[str, Any]:
+ raise NotImplementedError
+
+ def get_attrs(self) -> List[str]:
+ return ["molecule"]
+
+class MoleculeTransformersFeaturizer(MoleculeFeaturizer):
+ def __init__(self,
+ tokenizer: PreTrainedTokenizer,
+ max_length: int=512,
+ add_special_tokens: bool=True,
+ base: str='SMILES',
+ ) -> None:
+ super().__init__()
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+ self.add_special_tokens = add_special_tokens
+ self.base = base
+ if base not in ["SMILES", "SELFIES"]:
+ raise ValueError(f"{base} is not a valid 1D representaiton of molecules!")
+
+ def __call__(self, molecule: Molecule) -> BatchEncoding:
+ if self.base == "SMILES":
+ molecule._add_smiles()
+ parse_str = molecule.smiles
+ if self.base == "SELFIES":
+ molecule._add_selfies()
+ parse_str = molecule.selfies
+ return self.tokenizer(
+ parse_str,
+ max_length=self.max_length,
+ truncation=True,
+ add_special_tokens=self.add_special_tokens,
+ )
+
+class TextFeaturizer(Featurizer):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @abstractmethod
+ def __call__(self, text: Text) -> Dict[str, Any]:
+ raise NotImplementedError
+
+ def get_attrs(self) -> List[str]:
+ return ["text"]
+
+class TextTransformersFeaturizer(TextFeaturizer):
+ def __init__(self,
+ tokenizer: PreTrainedTokenizer,
+ max_length: int=512,
+ add_special_tokens: bool=True,
+ ) -> None:
+ super().__init__()
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+ self.add_special_tokens = add_special_tokens
+
+ def __call__(self, text: Text) -> BatchEncoding:
+ return self.tokenizer(
+ text.str,
+ max_length=self.max_length,
+ truncation=True,
+ add_special_tokens=self.add_special_tokens,
+ )
+
+class ProteinFeaturizer(Featurizer):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @abstractmethod
+ def __call__(self, protein: Protein) -> Dict[str, Any]:
+ raise NotImplementedError
+
+ def get_attrs(self) -> List[str]:
+ return ["protein"]
+
+class ProteinTransformersFeaturizer(ProteinFeaturizer):
+ def __init__(self,
+ tokenizer: PreTrainedTokenizer,
+ max_length: int=1024,
+ add_special_tokens: bool=True,
+ ) -> None:
+ super().__init__()
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+ self.add_special_tokens = add_special_tokens
+
+ def __call__(self, protein: Protein) -> Dict[str, Any]:
+ return self.tokenizer(
+ protein.sequence,
+ max_length=self.max_length,
+ truncation=True,
+ add_special_tokens=self.add_special_tokens,
+ )
+
+class PocketFeaturizer(Featurizer):
+ def __init__(self) -> None:
+ super().__init__()
+
+ @abstractmethod
+ def __call__(self, pocket: Pocket) -> Dict[str, Any]:
+ raise NotImplementedError
+
+ def get_attrs(self) -> List[str]:
+ return ["pocket"]
+
+# For classification tasks, directly convert numbers or arrays into tensors.
+class ClassLabelFeaturizer(Featurizer):
+ def __init__(self) -> None:
+ super().__init__()
+
+ # Input a number or an array, and return a tensor.
+ def __call__(self, label: np.array) -> torch.tensor:
+ return torch.tensor(label)
+
+ def get_attrs(self) -> List[str]:
+ return ["classlabel"]
+
+
+class EnsembleFeaturizer(Featurizer):
+ def __init__(self, to_ensemble: Dict[str, Featurizer]) -> None:
+ super().__init__()
+ self.featurizers = {}
+ for k, v in to_ensemble.items():
+ self.featurizers[k] = v
+
+ def __call__(self, **kwargs: Any) -> Dict[Any, Any]:
+ featurized = {}
+ for k, v in kwargs.items():
+ featurized[k] = self.featurizers[k](v)
+ return featurized
+
+ def get_attrs(self) -> List[str]:
+ return list(self.featurizers.keys())
+
+if __name__ == "__main__":
+ from transformers import DataCollatorWithPadding
+ featurizer = TextTransformersFeaturizer(T5Tokenizer.from_pretrained("./checkpoints/molt5/base"))
+ a = featurizer(text=Text.from_str("Hello"))
+ b = featurizer(text=Text.from_str("Hello World"))
+ collator = DataCollatorWithPadding(featurizer.tokenizer, padding=True)
+ print(collator([a, b]))
\ No newline at end of file
diff --git a/open_biomed/utils/logger.py b/open_biomed/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/open_biomed/utils/misc.py b/open_biomed/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a4507a1bd0ed0064833cf4cdf608779399d4339
--- /dev/null
+++ b/open_biomed/utils/misc.py
@@ -0,0 +1,114 @@
+from typing import Any, Dict, List, Optional, TextIO
+
+import numpy as np
+import random
+import torch
+import os
+from transformers import BatchEncoding, PreTrainedTokenizer
+
+from open_biomed.data import Molecule, Pocket, Protein, Text
+
+def sub_dict(in_dict: dict[str, Any], keys_to_include: List[str]) -> dict[str, Any]:
+ # Get a sub-dictionary based on keys_to_include
+ return {k: in_dict[k] for k in keys_to_include}
+
+def sub_batch_by_interval(start: int, end: int, **kwargs: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
+ # Construct a smaller batch from [start, end) of the original batch
+ new_batch = {}
+ for key in kwargs:
+ new_batch[key] = kwargs[key][start:end]
+ return new_batch
+
+def safe_index(l: List[Any], e: Any) -> int:
+ # Return index of element e in list l. If e is not present, return the last index
+ try:
+ return l.index(e)
+ except:
+ return len(l) - 1
+
+def concatenate_tokens(tokens_to_concat: List[BatchEncoding]) -> BatchEncoding:
+ # Concatenate multiple tokenized results by putting the non-padding tokens together
+ batch_size = tokens_to_concat[0].input_ids.shape[0]
+ concatenated = {
+ "input_ids": torch.cat([tokens.input_ids for tokens in tokens_to_concat], dim=-1),
+ "attention_mask": torch.cat([tokens.attention_mask for tokens in tokens_to_concat], dim=-1),
+ }
+ non_padding_length = concatenated["attention_mask"].sum(-1)
+ max_length = non_padding_length.max().item()
+
+ new_input_ids = []
+ new_attention_mask = []
+ for i in range(batch_size):
+ perm = torch.cat([
+ torch.where(concatenated["attention_mask"][i] == 1)[0], # non-padding tokens
+ torch.where(concatenated["attention_mask"][i] == 0)[0], # padding tokens
+ ])
+ new_input_ids.append(concatenated["input_ids"][i][perm[:max_length]])
+ new_attention_mask.append(concatenated["attention_mask"][i][perm[:max_length]])
+
+ return BatchEncoding({
+ "input_ids": torch.stack(new_input_ids, dim=0),
+ "attention_mask": torch.stack(new_attention_mask, dim=0),
+ })
+
+def collate_objects_as_list(inputs: List[Dict[str, Any]]) -> Dict[str, Any]:
+ outputs = {}
+ for sample in inputs:
+ for k, v in sample.items():
+ if k not in outputs:
+ outputs[k] = []
+ outputs[k].append(v)
+ return outputs
+
+def wrap_and_select_outputs(outputs: Any, context: Optional[TextIO]=None) -> Dict[str, Any]:
+ if isinstance(outputs, tuple):
+ outputs = outputs[0]
+ if isinstance(outputs, list):
+ if isinstance(outputs[0], list):
+ outputs = outputs[0]
+ selected = random.randint(0, len(outputs) - 1)
+ if len(outputs) > 1 and context is not None:
+ context.write(f"Selected {selected}th output for downstream tools.\n")
+ outputs = outputs[selected]
+ if isinstance(outputs, tuple):
+ wrapped_all = {}
+ for out in outputs:
+ wrapped = wrap_and_select_outputs(out)
+ for key, value in wrapped.items():
+ wrapped_all[key] = value
+ return wrapped_all
+ elif isinstance(outputs, Molecule):
+ return {"molecule": outputs}
+ elif isinstance(outputs, Protein):
+ return {"protein": outputs}
+ elif isinstance(outputs, Pocket):
+ return {"pocket": outputs}
+ elif isinstance(outputs, Text):
+ return {"text": outputs}
+ else:
+ return {"output": outputs}
+
+def create_tool_input(data_type: str, value: str) -> Any:
+ if data_type == "molecule":
+ if value.endswith(".sdf"):
+ return Molecule.from_sdf_file(value)
+ elif value.endswith(".pkl"):
+ return Molecule.from_binary_file(value)
+ else:
+ return Molecule.from_smiles(value)
+ elif data_type == "protein":
+ if value.endswith(".pdb"):
+ return Protein.from_pdb_file(value)
+ if os.path.exists(value.replace(".pkl", ".pdb")):
+ return Protein.from_pdb_file(value.replace(".pkl", ".pdb"))
+ if value.endswith(".pkl"):
+ return Protein.from_binary_file(value)
+ else:
+ return Protein.from_fasta(value)
+ elif data_type == "pocket":
+ return Pocket.from_binary_file(value)
+ elif data_type == "text":
+ return Text.from_str(value)
+ else:
+ return value
+
\ No newline at end of file
diff --git a/open_biomed/utils/mol_featurizer.py b/open_biomed/utils/mol_featurizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8824a951bb4c55f19cac05ccf05511ccb5a86c1c
--- /dev/null
+++ b/open_biomed/utils/mol_featurizer.py
@@ -0,0 +1,452 @@
+import logging
+logger = logging.getLogger(__name__)
+
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+import argparse
+import copy
+import json
+import numpy as np
+import pickle
+
+import torch
+
+import rdkit.Chem as Chem
+from rdkit.Chem import DataStructs, rdmolops
+from rdkit.Chem import AllChem, Descriptors
+from rdkit.Chem.rdMHFPFingerprint import MHFPEncoder
+from rdkit import RDLogger
+RDLogger.DisableLog("rdApp.*")
+
+from sklearn.preprocessing import OneHotEncoder
+from torch_geometric.data import Data
+from transformers import BertTokenizer, T5Tokenizer
+
+from .featurizer import MoleculeFeaturizer
+
+from open_biomed.data.molecule import Molecule
+
+
+def one_hot_encoding(x, allowable_set, encode_unknown=False):
+ """One-hot encoding.
+ """
+ if encode_unknown and (allowable_set[-1] is not None):
+ allowable_set.append(None)
+
+ if encode_unknown and (x not in allowable_set):
+ x = None
+
+ return list(map(lambda s: x == s, allowable_set))
+
+def safe_index(l, e):
+ """
+ Return index of element e in list l. If e is not present, return the last index
+ """
+ try:
+ return l.index(e)
+ except:
+ return len(l) - 1
+
+# Atom featurization: Borrowed from dgllife.utils.featurizers.py
+
+def atom_type_one_hot(atom, allowable_set=None, encode_unknown=False):
+ """One hot encoding for the type of an atom.
+ """
+ if allowable_set is None:
+ allowable_set = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca',
+ 'Fe', 'As', 'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb', 'Sb', 'Sn',
+ 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au',
+ 'Ni', 'Cd', 'In', 'Mn', 'Zr', 'Cr', 'Pt', 'Hg', 'Pb']
+ return one_hot_encoding(atom.GetSymbol(), allowable_set, encode_unknown)
+
+def atom_degree_one_hot(atom, allowable_set=None, encode_unknown=False):
+ """One hot encoding for the degree of an atom.
+ """
+ if allowable_set is None:
+ allowable_set = list(range(11))
+ return one_hot_encoding(atom.GetDegree(), allowable_set, encode_unknown)
+
+def atom_implicit_valence_one_hot(atom, allowable_set=None, encode_unknown=False):
+ """One hot encoding for the implicit valence of an atom.
+ """
+ if allowable_set is None:
+ allowable_set = list(range(7))
+ return one_hot_encoding(atom.GetImplicitValence(), allowable_set, encode_unknown)
+
+def atom_formal_charge(atom):
+ """Get formal charge for an atom.
+ """
+ return [atom.GetFormalCharge()]
+
+def atom_num_radical_electrons(atom):
+ return [atom.GetNumRadicalElectrons()]
+
+def atom_hybridization_one_hot(atom, allowable_set=None, encode_unknown=False):
+ """One hot encoding for the hybridization of an atom.
+ """
+ if allowable_set is None:
+ allowable_set = [Chem.rdchem.HybridizationType.SP,
+ Chem.rdchem.HybridizationType.SP2,
+ Chem.rdchem.HybridizationType.SP3,
+ Chem.rdchem.HybridizationType.SP3D,
+ Chem.rdchem.HybridizationType.SP3D2]
+ return one_hot_encoding(atom.GetHybridization(), allowable_set, encode_unknown)
+
+def atom_is_aromatic(atom):
+ """Get whether the atom is aromatic.
+ """
+ return [atom.GetIsAromatic()]
+
+def atom_total_num_H_one_hot(atom, allowable_set=None, encode_unknown=False):
+ """One hot encoding for the total number of Hs of an atom.
+ """
+ if allowable_set is None:
+ allowable_set = list(range(5))
+ return one_hot_encoding(atom.GetTotalNumHs(), allowable_set, encode_unknown)
+
+def atom_is_in_ring(atom):
+ """Get whether the atom is in ring.
+ """
+ return [atom.IsInRing()]
+
+def atom_chirality_type_one_hot(atom, allowable_set=None, encode_unknown=False):
+ """One hot encoding for the chirality type of an atom.
+ """
+ if not atom.HasProp('_CIPCode'):
+ return [False, False]
+
+ if allowable_set is None:
+ allowable_set = ['R', 'S']
+ return one_hot_encoding(atom.GetProp('_CIPCode'), allowable_set, encode_unknown)
+
+# Atom featurization: Borrowed from dgllife.utils.featurizers.py
+
+def bond_type_one_hot(bond, allowable_set=None, encode_unknown=False):
+ """One hot encoding for the type of a bond.
+ """
+ if allowable_set is None:
+ allowable_set = [Chem.rdchem.BondType.SINGLE,
+ Chem.rdchem.BondType.DOUBLE,
+ Chem.rdchem.BondType.TRIPLE,
+ Chem.rdchem.BondType.AROMATIC]
+ return one_hot_encoding(bond.GetBondType(), allowable_set, encode_unknown)
+
+class MolOneHotFeaturizer(MoleculeFeaturizer):
+ smiles_char = ['?', '#', '%', ')', '(', '+', '-', '.', '1', '0', '3', '2', '5', '4',
+ '7', '6', '9', '8', '=', 'A', 'C', 'B', 'E', 'D', 'G', 'F', 'I',
+ 'H', 'K', 'M', 'L', 'O', 'N', 'P', 'S', 'R', 'U', 'T', 'W', 'V',
+ 'Y', '[', 'Z', ']', '_', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i',
+ 'h', 'm', 'l', 'o', 'n', 's', 'r', 'u', 't', 'y']
+
+ def __init__(self, config):
+ super(MolOneHotFeaturizer, self).__init__()
+ self.max_len = config["max_len"]
+ self.enc = OneHotEncoder().fit(np.array(self.smiles_char).reshape(-1, 1))
+
+ def __call__(self, data):
+ temp = [c if c in self.smiles_char else '?' for c in data]
+ if len(temp) < self.max_len:
+ temp = temp + ['?'] * (self.max_len - len(temp))
+ else:
+ temp = temp[:self.max_len]
+ return torch.tensor(self.enc.transform(np.array(temp).reshape(-1, 1)).toarray().T)
+
+class MolGraphFeaturizer(MoleculeFeaturizer):
+ allowable_features = {
+ 'possible_atomic_num_list': list(range(1, 119)) + ['misc'],
+ 'possible_formal_charge_list': [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 'misc'],
+ 'possible_chirality_list': [
+ Chem.rdchem.ChiralType.CHI_UNSPECIFIED,
+ Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CW,
+ Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CCW,
+ Chem.rdchem.ChiralType.CHI_OTHER
+ ],
+ 'possible_hybridization_list': [
+ Chem.rdchem.HybridizationType.SP,
+ Chem.rdchem.HybridizationType.SP2,
+ Chem.rdchem.HybridizationType.SP3,
+ Chem.rdchem.HybridizationType.SP3D,
+ Chem.rdchem.HybridizationType.SP3D2,
+ Chem.rdchem.HybridizationType.UNSPECIFIED,
+ 'misc'
+ ],
+ 'possible_numH_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 'misc'],
+ 'possible_implicit_valence_list': [0, 1, 2, 3, 4, 5, 6],
+ 'possible_degree_list': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'misc'],
+ 'possible_number_radical_e_list': [0, 1, 2, 3, 4, 'misc'],
+ 'possible_is_aromatic_list': [False, True],
+ 'possible_is_in_ring_list': [False, True],
+ 'possible_bond_type_list': [
+ Chem.rdchem.BondType.SINGLE,
+ Chem.rdchem.BondType.DOUBLE,
+ Chem.rdchem.BondType.TRIPLE,
+ Chem.rdchem.BondType.AROMATIC,
+ 'misc'
+ ],
+ 'possible_bond_dirs': [ # only for double bond stereo information
+ Chem.rdchem.BondDir.NONE,
+ Chem.rdchem.BondDir.ENDUPRIGHT,
+ Chem.rdchem.BondDir.ENDDOWNRIGHT
+ ],
+ 'possible_bond_stereo_list': [
+ Chem.rdchem.BondStereo.STEREONONE,
+ Chem.rdchem.BondStereo.STEREOZ,
+ Chem.rdchem.BondStereo.STEREOE,
+ Chem.rdchem.BondStereo.STEREOCIS,
+ Chem.rdchem.BondStereo.STEREOTRANS,
+ Chem.rdchem.BondStereo.STEREOANY,
+ ],
+ 'possible_is_conjugated_list': [False, True]
+ }
+
+ def __init__(self, config):
+ super(MolGraphFeaturizer, self).__init__()
+ self.config = config
+ if self.config["name"] == "unimap":
+ self.allowable_features["possible_atomic_num_list"] = self.allowable_features["possible_atomic_num_list"][:-1] + ['[MASK]', 'misc']
+ self.allowable_features["possible_bond_type_list"] = self.allowable_features["possible_bond_type_list"][:-1] + ['[MASK]', '[SELF]', 'misc']
+ self.allowable_features["possible_bond_stereo_list"] = self.allowable_features["possible_bond_stereo_list"] + ['[MASK]']
+ self.allowable_features["possible_hybridization_list"] = self.allowable_features["possible_hybridization_list"][:-2] + ['misc']
+
+ def __call__(self, data):
+ if isinstance(data, Molecule):
+ mol = Chem.MolFromSmiles(data.smiles)
+ # mol = AllChem.MolFromSmiles(data)
+ else:
+ mol = data
+ # atoms
+ atom_features_list = []
+ for atom in mol.GetAtoms():
+ if self.config["name"] in ["ogb", "unimap"]:
+ atom_feature = [
+ safe_index(self.allowable_features['possible_atomic_num_list'], atom.GetAtomicNum()),
+ self.allowable_features['possible_chirality_list'].index(atom.GetChiralTag()),
+ safe_index(self.allowable_features['possible_degree_list'], atom.GetTotalDegree()),
+ safe_index(self.allowable_features['possible_formal_charge_list'], atom.GetFormalCharge()),
+ safe_index(self.allowable_features['possible_numH_list'], atom.GetTotalNumHs()),
+ safe_index(self.allowable_features['possible_number_radical_e_list'], atom.GetNumRadicalElectrons()),
+ safe_index(self.allowable_features['possible_hybridization_list'], atom.GetHybridization()),
+ self.allowable_features['possible_is_aromatic_list'].index(atom.GetIsAromatic()),
+ self.allowable_features['possible_is_in_ring_list'].index(atom.IsInRing()),
+ ]
+ else:
+ atom_feature = [
+ safe_index(self.allowable_features['possible_atomic_num_list'], atom.GetAtomicNum()),
+ self.allowable_features['possible_chirality_list'].index(atom.GetChiralTag())
+ ]
+ atom_features_list.append(atom_feature)
+ x = torch.tensor(np.array(atom_features_list), dtype=torch.long)
+
+ # bonds
+ if len(mol.GetBonds()) <= 0: # mol has no bonds
+ num_bond_features = 3 if self.config["name"] in ["ogb", "unimap"] else 2
+ edge_index = torch.empty((2, 0), dtype=torch.long)
+ edge_attr = torch.empty((0, num_bond_features), dtype=torch.long)
+ else: # mol has bonds
+ edges_list = []
+ edge_features_list = []
+ for bond in mol.GetBonds():
+ i = bond.GetBeginAtomIdx()
+ j = bond.GetEndAtomIdx()
+ if self.config["name"] in ["ogb", "unimap"]:
+ edge_feature = [
+ safe_index(self.allowable_features['possible_bond_type_list'], bond.GetBondType()),
+ self.allowable_features['possible_bond_stereo_list'].index(bond.GetStereo()),
+ self.allowable_features['possible_is_conjugated_list'].index(bond.GetIsConjugated()),
+ ]
+ else:
+ edge_feature = [
+ self.allowable_features['possible_bond_type_list'].index(bond.GetBondType()),
+ self.allowable_features['possible_bond_dirs'].index(bond.GetBondDir())
+ ]
+ edges_list.append((i, j))
+ edge_features_list.append(edge_feature)
+ edges_list.append((j, i))
+ edge_features_list.append(edge_feature)
+
+ # data.edge_index: Graph connectivity in COO format with shape [2, num_edges]
+ edge_index = torch.tensor(np.array(edges_list).T, dtype=torch.long)
+
+ # data.edge_attr: Edge feature matrix with shape [num_edges, num_edge_features]
+ edge_attr = torch.tensor(np.array(edge_features_list), dtype=torch.long)
+
+ data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
+
+ return data
+
+class MolGGNNFeaturizer(MoleculeFeaturizer):
+ def __init__(self, config):
+ super(MolGGNNFeaturizer, self).__init__()
+ self.max_n_atoms = config["max_n_atoms"]
+ self.atomic_num_list = config["atomic_num_list"]
+ self.bond_type_list = [
+ Chem.rdchem.BondType.SINGLE,
+ Chem.rdchem.BondType.DOUBLE,
+ Chem.rdchem.BondType.TRIPLE,
+ 'misc'
+ ]
+
+ def __call__(self, data):
+ if isinstance(data, str):
+ mol = Chem.MolFromSmiles(data)
+ else:
+ mol = data
+ Chem.Kekulize(mol)
+ x = self._construct_atomic_number_array(mol, self.max_n_atoms)
+ adj = self._construct_adj_matrix(mol, self.max_n_atoms)
+ return x, adj, self._rescale_adj(adj)
+
+ def _construct_atomic_number_array(self, mol, out_size=-1):
+ """Returns atomic numbers of atoms consisting a molecule.
+
+ Args:
+ mol (rdkit.Chem.Mol): Input molecule.
+ out_size (int): The size of returned array.
+ If this option is negative, it does not take any effect.
+ Otherwise, it must be larger than the number of atoms
+ in the input molecules. In that case, the tail of
+ the array is padded with zeros.
+
+ Returns:
+ torch.tensor: a tensor consisting of atomic numbers
+ of atoms in the molecule.
+ """
+
+ atom_list = [a.GetAtomicNum() for a in mol.GetAtoms()]
+ if len(atom_list) > self.max_n_atoms:
+ atom_list = atom_list[:self.max_n_atoms]
+
+ if out_size < 0:
+ result = torch.zeros(len(atom_list), len(self.atomic_num_list))
+ else:
+ result = torch.zeros(out_size, len(self.atomic_num_list))
+ for i, atom in enumerate(atom_list):
+ result[i, safe_index(self.atomic_num_list, atom)] = 1
+ for i in range(len(atom_list), self.max_n_atoms):
+ result[i, -1] = 1
+ return result
+
+ def _construct_adj_matrix(self, mol, out_size=-1, self_connection=True):
+ """Returns the adjacent matrix of the given molecule.
+
+ This function returns the adjacent matrix of the given molecule.
+ Contrary to the specification of
+ :func:`rdkit.Chem.rdmolops.GetAdjacencyMatrix`,
+ The diagonal entries of the returned matrix are all-one.
+
+ Args:
+ mol (rdkit.Chem.Mol): Input molecule.
+ out_size (int): The size of the returned matrix.
+ If this option is negative, it does not take any effect.
+ Otherwise, it must be larger than the number of atoms
+ in the input molecules. In that case, the adjacent
+ matrix is expanded and zeros are padded to right
+ columns and bottom rows.
+ self_connection (bool): Add self connection or not.
+ If True, diagonal element of adjacency matrix is filled with 1.
+
+ Returns:
+ adj (torch.tensor): The adjacent matrix of the input molecule.
+ It is 2-dimensional tensor with shape (atoms1, atoms2), where
+ atoms1 & atoms2 represent from and to of the edge respectively.
+ If ``out_size`` is non-negative, the returned
+ its size is equal to that value. Otherwise,
+ it is equal to the number of atoms in the the molecule.
+ """
+
+ if out_size < 0:
+ adj = torch.zeros(4, mol.GetNumAtoms(), mol.GetNumAtoms())
+ else:
+ adj = torch.zeros(4, out_size, out_size)
+ for bond in mol.GetBonds():
+ i = bond.GetBeginAtomIdx()
+ j = bond.GetEndAtomIdx()
+ adj[safe_index(self.bond_type_list, bond.GetBondType()), i, j] = 1
+ adj[safe_index(self.bond_type_list, bond.GetBondType()), j, i] = 1
+ adj[3] = 1 - torch.sum(adj[:3], dim=0)
+ return adj
+
+ def _rescale_adj(self, adj):
+ # Previous paper didn't use rescale_adj.
+ # In their implementation, the normalization sum is: num_neighbors = F.sum(adj, axis=(1, 2))
+ # In this implementation, the normaliztion term is different
+ # raise NotImplementedError
+ # (256,4,9, 9):
+ # 4: single, double, triple, and bond between disconnected atoms (negative mask of sum of previous)
+ # 1-adj[i,:3,:,:].sum(dim=0) == adj[i,4,:,:]
+ # usually first 3 matrices have no diagnal, the last has.
+ # A_prime = self.A + sp.eye(self.A.shape[0])
+ num_neighbors = adj.sum(dim=(0, 1)).float()
+ num_neighbors_inv = num_neighbors.pow(-1)
+ num_neighbors_inv[num_neighbors_inv == float('inf')] = 0
+ adj_prime = num_neighbors_inv[None, None, :] * adj
+ return adj_prime
+
+class MolMGNNFeaturizer(MoleculeFeaturizer):
+ allowable_atom_list = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na','Ca', 'Fe', 'As', 'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb','Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 'H','Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In', 'Mn', 'Zr','Cr', 'Pt', 'Hg', 'Pb', 'Unknown']
+ allowable_degree_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+ allowable_num_hs_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+ allowable_implicit_valence_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+ allowable_hybridization_list = [
+ Chem.rdchem.HybridizationType.SP,
+ Chem.rdchem.HybridizationType.SP2,
+ Chem.rdchem.HybridizationType.SP3,
+ Chem.rdchem.HybridizationType.SP3D,
+ Chem.rdchem.HybridizationType.SP3D2,
+ 'other'
+ ]
+ allowable_cip_code_list = ['R', 'S']
+
+ def __init__(self, config):
+ super(MolMGNNFeaturizer, self).__init__()
+ self.config = config
+
+ def __call__(self, data):
+ if isinstance(data, str):
+ mol = Chem.MolFromSmiles(data)
+ else:
+ mol = data
+
+ atom_features_list = []
+ for atom in mol.GetAtoms():
+ encoding = self.one_of_k_encoding_unk(atom.GetSymbol(), self.allowable_atom_list)
+ encoding += self.one_of_k_encoding(atom.GetDegree(), self.allowable_degree_list)
+ encoding += self.one_of_k_encoding_unk(atom.GetTotalNumHs(), self.allowable_num_hs_list)
+ encoding += self.one_of_k_encoding_unk(atom.GetImplicitValence(), self.allowable_implicit_valence_list)
+ encoding += self.one_of_k_encoding_unk(atom.GetHybridization(), self.allowable_hybridization_list)
+ encoding += [atom.GetIsAromatic()]
+ try:
+ encoding += self.one_of_k_encoding_unk(atom.GetProp("_CIPNode"), self.allowable_cip_code_list)
+ except:
+ encoding += [0, 0]
+ encoding += [atom.HasProp("_ChiralityPossible")]
+ encoding /= np.sum(encoding)
+ atom_features_list.append(encoding)
+ x = torch.tensor(np.array(atom_features_list), dtype=torch.float)
+
+ if len(mol.GetBonds()) <= 0:
+ edge_index = torch.empty((2, 0), dtype=torch.long)
+ else:
+ edges_list = []
+ for bond in mol.GetBonds():
+ i = bond.GetBeginAtomIdx()
+ j = bond.GetEndAtomIdx()
+ edges_list.append((i, j))
+ edges_list.append((j, i))
+ edge_index = torch.tensor(np.array(edges_list).T, dtype=torch.long)
+
+ return Data(x=x, edge_index=edge_index)
+
+ def one_of_k_encoding(self, x, allowable_set):
+ if x not in allowable_set:
+ raise Exception("input {0} not in allowable set{1}:".format(x, allowable_set))
+ return list(map(lambda s: x == s, allowable_set))
+
+ def one_of_k_encoding_unk(self, x, allowable_set):
+ """Maps inputs not in the allowable set to the last element."""
+ if x not in allowable_set:
+ x = allowable_set[-1]
+ return list(map(lambda s: x == s, allowable_set))
diff --git a/open_biomed/utils/prot_utils.py b/open_biomed/utils/prot_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca1f5489f058d574470bd291c59f3440a0d649c3
--- /dev/null
+++ b/open_biomed/utils/prot_utils.py
@@ -0,0 +1,16 @@
+import numpy as np
+import math
+
+def get_normalized_ctd(proteins):
+ from PyBioMed.PyProtein import CTD
+ ctds = []
+ for prot in proteins:
+ ctds.append(np.array(list(CTD.CalculateCTD(prot).values())))
+ ctds = np.array(ctds)
+ for i in range(ctds.shape[1]):
+ mean = np.mean(ctds[:, i])
+ var = np.var(ctds[:, i])
+ ctds[:, i] = (ctds[:, i] - mean) / math.sqrt(var)
+ for i in range(ctds.shape[0]):
+ ctds[i] /= np.linalg.norm(ctds[i])
+ return ctds
\ No newline at end of file
diff --git a/open_biomed/utils/split_utils.py b/open_biomed/utils/split_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf34eddbe13b489708def8405c9a118d95ba9d7a
--- /dev/null
+++ b/open_biomed/utils/split_utils.py
@@ -0,0 +1,180 @@
+import logging
+logger = logging.getLogger(__name__)
+
+import math
+import numpy as np
+from rdkit import Chem
+from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles
+import json
+import collections
+
+from open_biomed.utils.cluster import cluster_with_sim_matrix, merge_cluster
+from open_biomed.utils.prot_utils import get_normalized_ctd
+
+def random_split(n, r_val, r_test):
+ r_train = 1 - r_val - r_test
+ perm = np.random.permutation(n)
+ train_cutoff = r_train * n
+ val_cutoff = (r_train + r_val) * n
+ return perm[:train_cutoff], perm[train_cutoff : val_cutoff], perm[val_cutoff:]
+
+def kfold_split(n, k):
+ perm = np.random.permutation(n)
+ return [perm[i * n // k: (i + 1) * n // k] for i in range(k)]
+
+def _generate_scaffold(smiles, include_chirality=False, is_standard=False):
+ if is_standard:
+ scaffold = MurckoScaffoldSmiles(smiles=smiles, includeChirality=True)
+ else:
+ mol = Chem.MolFromSmiles(smiles)
+ scaffold = MurckoScaffoldSmiles(mol=mol, includeChirality=include_chirality)
+ return scaffold
+
+def generate_scaffolds(dataset, log_every_n=1000, sort=True, is_standard=False):
+ scaffolds = {}
+ data_len = len(dataset)
+
+ logger.info("About to generate scaffolds")
+ for ind, molecule in enumerate(dataset.molecules):
+ if log_every_n > 0 and ind % log_every_n == 0:
+ logger.info("Generating scaffold %d/%d" % (ind, data_len))
+ # TODO: z
+ scaffold = _generate_scaffold(molecule.smiles, is_standard=is_standard)
+ if scaffold not in scaffolds:
+ scaffolds[scaffold] = [ind]
+ else:
+ scaffolds[scaffold].append(ind)
+
+ if sort:
+ # Sort from largest to smallest scaffold sets
+ scaffolds = {key: sorted(value) for key, value in scaffolds.items()}
+ scaffold_sets = [
+ scaffold_set for (scaffold, scaffold_set) in sorted(
+ scaffolds.items(),
+ key=lambda x: (len(x[1]), x[1][0]),
+ reverse=True
+ )
+ ]
+ else:
+ scaffold_sets = [value for key, value in scaffolds.items()]
+
+ # TODO: DEBUG
+ """
+ scaffold_index = collections.OrderedDict()
+ for i, value in enumerate(scaffold_sets):
+ scaffold_index[i] = str(value)
+ scaffold_index = json.dumps(scaffold_index)
+ with open("scaffold_set_2.json","w") as f:
+ f.write(scaffold_index)
+ """
+ return scaffold_sets
+
+def scaffold_split(dataset, r_val, r_test, log_every_n=1000, is_standard=False):
+ r_train = 1.0 - r_val - r_test
+ scaffold_sets = generate_scaffolds(dataset, log_every_n, is_standard=is_standard)
+
+ train_cutoff = r_train * len(dataset)
+ valid_cutoff = (r_train + r_val) * len(dataset)
+ train_inds = []
+ valid_inds = []
+ test_inds = []
+
+ logger.info("About to sort in scaffold sets")
+ for scaffold_set in scaffold_sets:
+ if len(train_inds) + len(scaffold_set) > train_cutoff:
+ if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff:
+ test_inds += scaffold_set
+ else:
+ valid_inds += scaffold_set
+ else:
+ train_inds += scaffold_set
+ return train_inds, valid_inds, test_inds
+
+def cold_drug_split(dataset, nfolds):
+ scaffold_sets = generate_scaffolds(dataset, -1, sort=False)
+ n_cutoff = len(dataset.pair_index) // nfolds
+ drug_pair_index = {}
+ for i, (i_drug, i_prot) in enumerate(dataset.pair_index):
+ if i_drug not in drug_pair_index:
+ drug_pair_index[i_drug] = [i]
+ else:
+ drug_pair_index[i_drug].append(i)
+
+ folds = [[] for i in range(nfolds)]
+ cur = 0
+ for scaffold_set in scaffold_sets:
+ pair_in_scaffold_set = []
+ for i_drug in scaffold_set:
+ pair_in_scaffold_set += drug_pair_index[i_drug]
+ if cur != nfolds - 1 and len(folds[cur]) + len(pair_in_scaffold_set) >= n_cutoff:
+ if len(folds[cur]) + len(pair_in_scaffold_set) - n_cutoff > n_cutoff - len(folds[cur]):
+ cur += 1
+ folds[cur] += pair_in_scaffold_set
+ else:
+ folds[cur] += pair_in_scaffold_set
+ cur += 1
+ else:
+ folds[cur] += pair_in_scaffold_set
+ return folds
+
+def cold_protein_split(dataset, nfolds):
+ ctds = get_normalized_ctd(dataset.proteins)
+ prot_sim = ctds @ ctds.T
+ clusters = cluster_with_sim_matrix(prot_sim, 0.3)
+
+ prot_pair_index = {}
+ for i, (i_drug, i_prot) in enumerate(dataset.pair_index):
+ if i_prot not in prot_pair_index:
+ prot_pair_index[i_prot] = [i]
+ else:
+ prot_pair_index[i_prot].append(i)
+
+ n_cutoff = len(dataset.pair_index) // nfolds
+ folds = [[] for i in range(nfolds)]
+ cur = 0
+ for cluster in clusters:
+ pair_in_cluster = []
+ for i_protein in cluster:
+ if i_protein in prot_pair_index:
+ pair_in_cluster += prot_pair_index[i_protein]
+ if cur != nfolds - 1 and len(folds[cur]) + len(pair_in_cluster) >= n_cutoff:
+ if len(folds[cur]) + len(pair_in_cluster) - n_cutoff > n_cutoff - len(folds[cur]):
+ cur += 1
+ folds[cur] += pair_in_cluster
+ else:
+ folds[cur] += pair_in_cluster
+ cur += 1
+ else:
+ folds[cur] += pair_in_cluster
+ return folds
+
+def cold_cluster_split(dataset, ngrids):
+ drug_clusters = generate_scaffolds(dataset, -1)
+ drug_clusters = merge_cluster(drug_clusters, ngrids)
+
+ ctds = get_normalized_ctd(dataset.proteins)
+ prot_sim = ctds @ ctds.T
+ prot_clusters = cluster_with_sim_matrix(prot_sim, 0.3)
+ prot_clusters = merge_cluster(prot_clusters, ngrids)
+
+ pair_in_grid = []
+ for i in range(ngrids):
+ pair_in_grid.append([])
+ for j in range(ngrids):
+ pair_in_grid[i].append([])
+ for k, (i_drug, i_prot) in enumerate(dataset.pair_index):
+ if i_drug in drug_clusters[i] and i_prot in prot_clusters[j]:
+ pair_in_grid[i][j].append(k)
+
+ folds = []
+ for i in range(ngrids):
+ for j in range(ngrids):
+ folds.append({"test": pair_in_grid[i][j]})
+ train = []
+ for k in range(ngrids):
+ if k != i:
+ for l in range(ngrids):
+ if l != j:
+ train += pair_in_grid[k][l]
+ folds[-1]["train"] = train
+ return folds
\ No newline at end of file
diff --git a/predictions_test.csv b/predictions_test.csv
deleted file mode 100644
index 74b11a6606cd1f51cea5d5e4cedcc32c10f221f5..0000000000000000000000000000000000000000
--- a/predictions_test.csv
+++ /dev/null
@@ -1,3007 +0,0 @@
-y_true,y_pred
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..de6eea7c775795bfa478753d176b2265f27c0476
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,23 @@
+torch_geometric==2.5.2
+transformers==4.38.0
+rdkit==2023.09.5
+sentencepiece==0.2.0
+selfies==2.1.2
+pandas==2.2.3
+lightning_utilities==0.11.9
+torchmetrics==1.5.2
+numpy==1.26.4
+absl-py==2.1.0
+easydict==1.13
+ratelimiter==1.2.0.post0
+openai==1.64.0
+uvicorn===0.32.1
+fastapi===0.115.5
+oss2==2.19.1
+dotenv==1.0.1
+protobuf==5.28.3
+scanpy==1.10.3
+qdrant-client>=1.7.0
+streamlit>=1.28.0
+plotly>=5.18.0
+scikit-learn>=1.3.0
diff --git a/runs/20260124_212915_DAVIS/test_markdowntable.txt b/runs/20260124_212915_DAVIS/test_markdowntable.txt
deleted file mode 100644
index e3738cf0333e3cd80e49daac66eab6e744061f0b..0000000000000000000000000000000000000000
--- a/runs/20260124_212915_DAVIS/test_markdowntable.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-+--------+---------------------+--------------+-------------------+
-| MSE | Pearson Correlation | with p-value | Concordance Index |
-+--------+---------------------+--------------+-------------------+
-| 0.4734 | 0.5386 | 0.0000 | 0.7885 |
-+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/runs/20260124_212915_DAVIS/valid_markdowntable.txt b/runs/20260124_212915_DAVIS/valid_markdowntable.txt
deleted file mode 100644
index a8ba554bd0b3dc2defd205af4b5f35f51bffce12..0000000000000000000000000000000000000000
--- a/runs/20260124_212915_DAVIS/valid_markdowntable.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-+---------+--------+---------------------+--------------+-------------------+
-| # epoch | MSE | Pearson Correlation | with p-value | Concordance Index |
-+---------+--------+---------------------+--------------+-------------------+
-| epoch 0 | 0.5978 | 0.4531 | 0.0000 | 0.7311 |
-| epoch 1 | 0.5074 | 0.5377 | 0.0000 | 0.7823 |
-| epoch 2 | 0.4985 | 0.5556 | 0.0000 | 0.7877 |
-| epoch 3 | 0.4908 | 0.5612 | 0.0000 | 0.7901 |
-| epoch 4 | 0.4868 | 0.5670 | 0.0000 | 0.7932 |
-| epoch 5 | 0.4889 | 0.5699 | 0.0000 | 0.7948 |
-| epoch 6 | 0.4791 | 0.5761 | 0.0000 | 0.7981 |
-| epoch 7 | 0.4769 | 0.5801 | 0.0000 | 0.8019 |
-| epoch 8 | 0.4714 | 0.5875 | 0.0000 | 0.8053 |
-| epoch 9 | 0.4764 | 0.5889 | 0.0000 | 0.8081 |
-+---------+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/runs/20260124_224109_KIBA/predictions_test.csv b/runs/20260124_224109_KIBA/predictions_test.csv
deleted file mode 100644
index 08437b8bf2f3b52c0a37f40057e1e2aa205a55c2..0000000000000000000000000000000000000000
--- a/runs/20260124_224109_KIBA/predictions_test.csv
+++ /dev/null
@@ -1,11767 +0,0 @@
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diff --git a/runs/20260124_224109_KIBA/test_markdowntable.txt b/runs/20260124_224109_KIBA/test_markdowntable.txt
deleted file mode 100644
index a564612b709131d441e4086583d49b7eff122b15..0000000000000000000000000000000000000000
--- a/runs/20260124_224109_KIBA/test_markdowntable.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-+--------+---------------------+--------------+-------------------+
-| MSE | Pearson Correlation | with p-value | Concordance Index |
-+--------+---------------------+--------------+-------------------+
-| 0.0008 | 0.4817 | 0.0000 | 0.6847 |
-+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/runs/20260124_224109_KIBA/valid_markdowntable.txt b/runs/20260124_224109_KIBA/valid_markdowntable.txt
deleted file mode 100644
index ef1883239177fee440d2ef88950fa2013d6ef46a..0000000000000000000000000000000000000000
--- a/runs/20260124_224109_KIBA/valid_markdowntable.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-+---------+--------+---------------------+--------------+-------------------+
-| # epoch | MSE | Pearson Correlation | with p-value | Concordance Index |
-+---------+--------+---------------------+--------------+-------------------+
-| epoch 0 | 0.0046 | -0.0476 | 0.0000 | 0.4710 |
-| epoch 1 | 0.0025 | 0.1240 | 0.0000 | 0.5352 |
-| epoch 2 | 0.0052 | 0.2611 | 0.0000 | 0.5879 |
-| epoch 3 | 0.0030 | 0.3712 | 0.0000 | 0.6374 |
-| epoch 4 | 0.0009 | 0.4274 | 0.0000 | 0.6673 |
-| epoch 5 | 0.0038 | 0.4554 | 0.0000 | 0.6727 |
-| epoch 6 | 0.0008 | 0.4896 | 0.0000 | 0.6897 |
-| epoch 7 | 0.0010 | 0.5113 | 0.0000 | 0.7005 |
-| epoch 8 | 0.0112 | 0.5019 | 0.0000 | 0.6889 |
-| epoch 9 | 0.0012 | 0.5242 | 0.0000 | 0.7023 |
-+---------+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/runs/20260125_080409_BindingDB_Kd/predictions_test.csv b/runs/20260125_080409_BindingDB_Kd/predictions_test.csv
deleted file mode 100644
index 91f9aaaa7419717a8de0edf5ff672fa2727cc541..0000000000000000000000000000000000000000
--- a/runs/20260125_080409_BindingDB_Kd/predictions_test.csv
+++ /dev/null
@@ -1,4224 +0,0 @@
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diff --git a/runs/20260125_080409_BindingDB_Kd/test_markdowntable.txt b/runs/20260125_080409_BindingDB_Kd/test_markdowntable.txt
deleted file mode 100644
index cf9ef9bbe767f263c10c32cb99d03d79782f9e2e..0000000000000000000000000000000000000000
--- a/runs/20260125_080409_BindingDB_Kd/test_markdowntable.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-+--------+---------------------+--------------+-------------------+
-| MSE | Pearson Correlation | with p-value | Concordance Index |
-+--------+---------------------+--------------+-------------------+
-| 0.6668 | 0.7679 | 0.0000 | 0.8083 |
-+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/runs/20260125_080409_BindingDB_Kd/valid_markdowntable.txt b/runs/20260125_080409_BindingDB_Kd/valid_markdowntable.txt
deleted file mode 100644
index 75d1ff0eddcd9a0f6014ddb1a0a5a33b4b066979..0000000000000000000000000000000000000000
--- a/runs/20260125_080409_BindingDB_Kd/valid_markdowntable.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-+---------+--------+---------------------+--------------+-------------------+
-| # epoch | MSE | Pearson Correlation | with p-value | Concordance Index |
-+---------+--------+---------------------+--------------+-------------------+
-| epoch 0 | 1.2659 | 0.4823 | 0.0000 | 0.6973 |
-| epoch 1 | 0.9707 | 0.6357 | 0.0000 | 0.7518 |
-| epoch 2 | 0.8869 | 0.6826 | 0.0000 | 0.7689 |
-| epoch 3 | 0.8159 | 0.7071 | 0.0000 | 0.7817 |
-| epoch 4 | 0.7900 | 0.7184 | 0.0000 | 0.7847 |
-| epoch 5 | 0.7666 | 0.7283 | 0.0000 | 0.7905 |
-| epoch 6 | 0.7733 | 0.7384 | 0.0000 | 0.7980 |
-| epoch 7 | 0.7346 | 0.7413 | 0.0000 | 0.7972 |
-| epoch 8 | 0.7111 | 0.7544 | 0.0000 | 0.8047 |
-| epoch 9 | 0.6937 | 0.7586 | 0.0000 | 0.8065 |
-+---------+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/runs/20260125_104915_KIBA/model.pt b/runs/20260125_104915_KIBA/model.pt
deleted file mode 100644
index 1d1b66181722c996c79907b13d5faa7004fd2232..0000000000000000000000000000000000000000
--- a/runs/20260125_104915_KIBA/model.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/runs/20260125_104915_KIBA/predictions_test.csv b/runs/20260125_104915_KIBA/predictions_test.csv
deleted file mode 100644
index 997292b02fd8d361341929e5533df9653877d5b6..0000000000000000000000000000000000000000
--- a/runs/20260125_104915_KIBA/predictions_test.csv
+++ /dev/null
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diff --git a/runs/20260125_104915_KIBA/test_markdowntable.txt b/runs/20260125_104915_KIBA/test_markdowntable.txt
deleted file mode 100644
index 0aa876ca22bbb27dc8fe42f949ded12eed2ca413..0000000000000000000000000000000000000000
--- a/runs/20260125_104915_KIBA/test_markdowntable.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-+--------+---------------------+--------------+-------------------+
-| MSE | Pearson Correlation | with p-value | Concordance Index |
-+--------+---------------------+--------------+-------------------+
-| 0.0008 | 0.5219 | 0.0000 | 0.7003 |
-+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/runs/20260125_104915_KIBA/valid_markdowntable.txt b/runs/20260125_104915_KIBA/valid_markdowntable.txt
deleted file mode 100644
index 4e340194b1d604287d623f010c0413ed10a4aa91..0000000000000000000000000000000000000000
--- a/runs/20260125_104915_KIBA/valid_markdowntable.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-+---------+--------+---------------------+--------------+-------------------+
-| # epoch | MSE | Pearson Correlation | with p-value | Concordance Index |
-+---------+--------+---------------------+--------------+-------------------+
-| epoch 0 | 0.0009 | 0.4542 | 0.0000 | 0.6730 |
-| epoch 1 | 0.0179 | 0.4802 | 0.0000 | 0.6799 |
-| epoch 2 | 0.0076 | 0.5230 | 0.0000 | 0.7055 |
-| epoch 3 | 0.0022 | 0.5346 | 0.0000 | 0.7067 |
-| epoch 4 | 0.0008 | 0.5291 | 0.0000 | 0.7009 |
-| epoch 5 | 0.0043 | 0.5578 | 0.0000 | 0.7056 |
-| epoch 6 | 0.0056 | 0.5834 | 0.0000 | 0.7128 |
-| epoch 7 | 0.0168 | 0.5591 | 0.0000 | 0.6958 |
-| epoch 8 | 0.0009 | 0.5639 | 0.0000 | 0.7024 |
-| epoch 9 | 0.0160 | 0.5796 | 0.0000 | 0.7106 |
-+---------+--------+---------------------+--------------+-------------------+
\ No newline at end of file
diff --git a/scripts/bioflow_obm.py b/scripts/bioflow_obm.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2035cb954482bcbd2b917af4978770d86cc0d00
--- /dev/null
+++ b/scripts/bioflow_obm.py
@@ -0,0 +1,36 @@
+import os
+import sys
+import torch
+import numpy as np
+import logging
+from typing import List, Union, Dict, Any, Optional
+from tqdm import tqdm
+
+# Add root to python path to allow imports from open_biomed
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Removed BioMedGPT references and added placeholders for open-source models
+class BioFlowOBM:
+ def __init__(self, config_path: str = "configs/model/opensource_model.yaml"):
+ self.config_path = config_path
+ self.model = None # Placeholder for open-source model
+
+ def initialize(self):
+ # Placeholder for initializing open-source model
+ pass
+
+ def process_data(self, data):
+ # Placeholder for processing data using open-source model
+ pass
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Example usage block
+if __name__ == "__main__":
+ # Create valid dummy data/config for test if needed
+ print("This script is a library. Import OBM to use.")
+ print("Example:")
+ print("from scripts.bioflow_obm import OBM")
+ print("obm = OBM()")
+ print("vec = obm.encode_text('Biology is complex')")
diff --git a/scripts/debug.sh b/scripts/debug.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0ed047aedcea441c597d15c297eef3fc240e611f
--- /dev/null
+++ b/scripts/debug.sh
@@ -0,0 +1,13 @@
+#!bin/bash
+export CUDA_VISIBLE_DEVICES=$4
+TASK=$1
+MODEL=$2
+DATASET=$3
+
+python open_biomed/scripts/train.py \
+--task $TASK \
+--additional_config_file configs/model/$MODEL.yaml \
+--dataset_name $DATASET \
+--dataset_path ./datasets/$TASK/$DATASET \
+--empty_folder \
+--debug
\ No newline at end of file
diff --git a/scripts/run_docker.sh b/scripts/run_docker.sh
new file mode 100644
index 0000000000000000000000000000000000000000..823b429e3f2962a3391f73a81829f2d4272203be
--- /dev/null
+++ b/scripts/run_docker.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+# docker build
+docker build -t openbiomed .
+
+# docker run
+docker stop openbiomed_container && docker rm openbiomed_container
+docker run -it -d --gpus all -p 8082:8082 -p 8083:8083 -v /root/code/OpenBioMed:/app --name openbiomed_container openbiomed:latest
\ No newline at end of file
diff --git a/scripts/run_server.sh b/scripts/run_server.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a5a5587ff42fd75054a757b0ef55ce025dc46146
--- /dev/null
+++ b/scripts/run_server.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+echo "server start"
+
+# Activate the Conda environment
+source /root/miniconda3/bin/activate OpenBioMed
+
+python -m uvicorn open_biomed.scripts.run_server:app \
+ --host 0.0.0.0 \
+ --port 8082 \
+ --log-level info > ./tmp/server.log 2>&1 &
+
+python -m uvicorn open_biomed.scripts.run_server_workflow:app \
+ --host 0.0.0.0 \
+ --port 8083 \
+ --log-level info > ./tmp/workflow.log 2>&1 &
+
+tail -f /dev/null
\ No newline at end of file
diff --git a/scripts/test.sh b/scripts/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..479461456775013512a6740e788730f5ebd4d9af
--- /dev/null
+++ b/scripts/test.sh
@@ -0,0 +1,14 @@
+#!bin/bash
+export CUDA_VISIBLE_DEVICES=$4
+TASK=$1
+MODEL=$2
+DATASET=$3
+
+python open_biomed/scripts/train.py \
+--task $TASK \
+--additional_config_file configs/model/$MODEL.yaml \
+--dataset_name $DATASET \
+--dataset_path ./datasets/$TASK/$DATASET \
+--ckpt_path ./checkpoints/server/pharmolix_fm.ckpt \
+--batch_size_eval 32 \
+--test_only
\ No newline at end of file
diff --git a/scripts/train.sh b/scripts/train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..93d410b928c78155e10f12d56acbda01a88b2376
--- /dev/null
+++ b/scripts/train.sh
@@ -0,0 +1,12 @@
+#!bin/bash
+export CUDA_VISIBLE_DEVICES=$4
+TASK=$1
+MODEL=$2
+DATASET=$3
+
+python open_biomed/scripts/train.py \
+--task $TASK \
+--additional_config_file configs/model/$MODEL.yaml \
+--dataset_name $DATASET \
+--dataset_path ./datasets/$TASK/$DATASET \
+--empty_folder
\ No newline at end of file
diff --git a/scripts/verify_phase1.py b/scripts/verify_phase1.py
new file mode 100644
index 0000000000000000000000000000000000000000..09bc559ef5c0177104d81c8dad7c4339374df46a
--- /dev/null
+++ b/scripts/verify_phase1.py
@@ -0,0 +1,81 @@
+
+import sys
+import os
+from pathlib import Path
+
+# Add project root to path
+sys.path.append(str(Path(__file__).parent.parent))
+
+from bioflow.core import (
+ BioFlowOrchestrator,
+ ToolRegistry,
+ WorkflowConfig,
+ Modality,
+ BioEncoder,
+ BioPredictor,
+ BioRetriever,
+ EmbeddingResult,
+ PredictionResult,
+ RetrievalResult
+)
+
+# 1. Create Mock Tools for Verification
+class MockEncoder(BioEncoder):
+ def encode(self, content, modality):
+ print(f" [Mock] Encoding {modality.value}: {content[:20]}...")
+ return EmbeddingResult(vector=[0.1]*768, modality=modality, dimension=768)
+ @property
+ def dimension(self): return 768
+
+class MockPredictor(BioPredictor):
+ def predict(self, drug, target):
+ print(f" [Mock] Predicting interaction for drug candidate...")
+ return PredictionResult(score=0.85, confidence=0.9)
+
+class MockRetriever(BioRetriever):
+ def search(self, query, limit=10, filters=None, **kwargs):
+ print(f" [Mock] Searching Vector DB for neighbors...")
+ return [RetrievalResult(id="mol_1", score=0.95, content="CCO", modality=Modality.SMILES)]
+ def ingest(self, content, modality, payload=None): return "1"
+
+# 2. Setup Registry
+print("--- 🛠️ Step 1: Tool Registry Setup ---")
+ToolRegistry.register_encoder("obm", MockEncoder())
+ToolRegistry.register_predictor("deeppurpose", MockPredictor())
+print(ToolRegistry.summary())
+print()
+
+# 3. Load and Visualize Workflow
+print("--- 🗺️ Step 2: Workflow DAG Resolution ---")
+workflow_path = Path("bioflow/workflows/drug_discovery.yaml")
+import yaml
+with open(workflow_path, 'r') as f:
+ config_dict = yaml.safe_load(f)
+
+orchestrator = BioFlowOrchestrator()
+orchestrator.set_retriever(MockRetriever()) # Configured the retriever
+config = WorkflowConfig.from_dict(config_dict)
+orchestrator.register_workflow(config)
+
+# Show topological order
+order = orchestrator._build_execution_order(config)
+print(f"Workflow: {config.name}")
+print("Execution Sequence (Topological Sort):")
+for i, node in enumerate(order):
+ deps = f" (depends on: {', '.join(node.inputs)})" if node.inputs else ""
+ print(f" {i+1}. [{node.id}] Type: {node.type.value}{deps}")
+print()
+
+# 4. Perform Dry Run
+print("--- 🚀 Step 3: Pipeline Execution Dry Run ---")
+input_smiles = "CN1C=NC2=C1C(=O)N(C(=O)N2C)C" # Caffeine
+result = orchestrator.run("drug_discovery_basic", input_smiles)
+
+print("\n--- 📊 Final Report ---")
+if result.success:
+ print(f"Status: ✅ SUCCESS")
+ print(f"Final Output Score: {result.output[0].score if isinstance(result.output, list) else result.output.score}")
+ print(f"Duration: {result.duration_ms:.2f} ms")
+else:
+ print(f"Status: ❌ FAILED")
+ print(f"Errors: {result.context.errors}")
diff --git a/scripts/verify_phase2.py b/scripts/verify_phase2.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a177d61e1d7a0981a342c3cd1c52fae6a810bba
--- /dev/null
+++ b/scripts/verify_phase2.py
@@ -0,0 +1,273 @@
+"""
+Phase 2 Verification Script
+=============================
+
+Tests the real open-source encoder implementations.
+
+Usage:
+ python scripts/verify_phase2.py [--full]
+
+ --full: Run full tests with model downloads (slow, requires GPU recommended)
+
+Without --full, runs quick tests with fallback/mock behavior.
+"""
+
+import sys
+import os
+from pathlib import Path
+
+# Add project root
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import logging
+logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+logger = logging.getLogger(__name__)
+
+
+def test_registry():
+ """Test that all plugins are importable and registry works."""
+ print("\n" + "="*60)
+ print("🛠️ TEST 1: Plugin Registry")
+ print("="*60)
+
+ from bioflow.core import ToolRegistry, Modality
+ from bioflow.plugins import (
+ OBMEncoder,
+ TextEncoder,
+ MoleculeEncoder,
+ ProteinEncoder,
+ QdrantRetriever,
+ DeepPurposePredictor
+ )
+
+ print("✅ All plugins imported successfully")
+
+ # List available
+ print("\nAvailable plugins:")
+ print(" • OBMEncoder (multimodal)")
+ print(" • TextEncoder (PubMedBERT/SciBERT)")
+ print(" • MoleculeEncoder (ChemBERTa/RDKit)")
+ print(" • ProteinEncoder (ESM-2/ProtBERT)")
+ print(" • QdrantRetriever (Qdrant)")
+ print(" • DeepPurposePredictor (DTI)")
+
+ return True
+
+
+def test_rdkit_fallback():
+ """Test RDKit molecule encoding (no GPU needed)."""
+ print("\n" + "="*60)
+ print("🧪 TEST 2: RDKit Molecule Encoder (CPU-only)")
+ print("="*60)
+
+ try:
+ from bioflow.plugins.encoders.molecule_encoder import MoleculeEncoder
+ from bioflow.core import Modality
+
+ encoder = MoleculeEncoder(backend="rdkit_morgan", fp_size=2048)
+
+ test_molecules = [
+ ("CCO", "Ethanol"),
+ ("CC(=O)Oc1ccccc1C(=O)O", "Aspirin"),
+ ("CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "Caffeine"),
+ ]
+
+ print(f"Encoder dimension: {encoder.dimension}")
+ print("\nEncoding molecules:")
+
+ for smiles, name in test_molecules:
+ result = encoder.encode(smiles, Modality.SMILES)
+ nonzero = sum(1 for v in result.vector if v > 0)
+ print(f" • {name}: {nonzero} bits set (of {len(result.vector)})")
+
+ print("✅ RDKit encoding works!")
+ return True
+
+ except ImportError as e:
+ print(f"⚠️ RDKit not installed: {e}")
+ print(" Install with: pip install rdkit")
+ return False
+
+
+def test_deeppurpose_predictor():
+ """Test DeepPurpose predictor (with fallback)."""
+ print("\n" + "="*60)
+ print("🔮 TEST 3: DeepPurpose Predictor")
+ print("="*60)
+
+ from bioflow.plugins.deeppurpose_predictor import DeepPurposePredictor
+
+ predictor = DeepPurposePredictor()
+
+ # Test data
+ drug = "CC(=O)Oc1ccccc1C(=O)O" # Aspirin
+ target = "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"
+
+ print(f"Drug: Aspirin (SMILES: {drug[:30]}...)")
+ print(f"Target: Protein ({len(target)} amino acids)")
+
+ result = predictor.predict(drug, target)
+
+ print(f"\nPrediction:")
+ print(f" • Score: {result.score:.3f}")
+ print(f" • Label: {result.label}")
+ print(f" • Confidence: {result.confidence:.2f}")
+ print(f" • Method: {result.metadata.get('method', 'unknown')}")
+
+ if result.metadata.get('warning'):
+ print(f" ⚠️ {result.metadata['warning']}")
+
+ print("✅ Predictor works (with fallback if DeepPurpose unavailable)")
+ return True
+
+
+def test_qdrant_retriever():
+ """Test Qdrant retriever with mock encoder."""
+ print("\n" + "="*60)
+ print("🗄️ TEST 4: Qdrant Retriever (In-Memory)")
+ print("="*60)
+
+ try:
+ from bioflow.core import BioEncoder, Modality, EmbeddingResult
+ from bioflow.plugins.qdrant_retriever import QdrantRetriever
+
+ # Mock encoder for testing
+ class MockEncoder(BioEncoder):
+ def encode(self, content, modality):
+ # Simple hash-based vector
+ import hashlib
+ h = hashlib.md5(content.encode()).hexdigest()
+ vector = [int(c, 16) / 15.0 for c in h] * 48 # 768-dim
+ return EmbeddingResult(vector=vector[:768], modality=modality, dimension=768)
+
+ @property
+ def dimension(self): return 768
+
+ encoder = MockEncoder()
+ retriever = QdrantRetriever(encoder=encoder, collection="test_molecules")
+
+ # Ingest test data
+ test_data = [
+ ("CCO", "Ethanol", {"type": "alcohol"}),
+ ("CCCO", "Propanol", {"type": "alcohol"}),
+ ("CC(=O)O", "Acetic acid", {"type": "acid"}),
+ ("c1ccccc1", "Benzene", {"type": "aromatic"}),
+ ]
+
+ print("Ingesting molecules...")
+ for smiles, name, payload in test_data:
+ retriever.ingest(smiles, Modality.SMILES, {"name": name, **payload})
+
+ print(f"Collection size: {retriever.count()}")
+
+ # Search
+ print("\nSearching for 'CCCCO' (Butanol)...")
+ results = retriever.search("CCCCO", limit=3, modality=Modality.SMILES)
+
+ print("Results:")
+ for r in results:
+ print(f" • {r.payload.get('name', 'Unknown')}: score={r.score:.3f}")
+
+ print("✅ Qdrant retriever works!")
+ return True
+
+ except ImportError as e:
+ print(f"⚠️ qdrant-client not installed: {e}")
+ print(" Install with: pip install qdrant-client")
+ return False
+
+
+def test_full_obm_encoder():
+ """Test full OBM encoder with real models (slow, requires downloads)."""
+ print("\n" + "="*60)
+ print("🚀 TEST 5: Full OBM Encoder (requires model downloads)")
+ print("="*60)
+
+ try:
+ from bioflow.plugins.obm_encoder import OBMEncoder
+ from bioflow.core import Modality
+
+ print("Initializing OBMEncoder...")
+ print("(This will download models on first run - ~500MB)")
+
+ obm = OBMEncoder(
+ text_model="pubmedbert",
+ molecule_model="chemberta",
+ protein_model="esm2_t6", # Smallest ESM model
+ lazy_load=True
+ )
+
+ # Test text
+ print("\n1. Encoding text...")
+ text_result = obm.encode("EGFR inhibitor for lung cancer treatment", Modality.TEXT)
+ print(f" Text embedding: {len(text_result.vector)} dims")
+
+ # Test molecule
+ print("2. Encoding molecule...")
+ mol_result = obm.encode("CC(=O)Oc1ccccc1C(=O)O", Modality.SMILES)
+ print(f" Molecule embedding: {len(mol_result.vector)} dims")
+
+ # Test protein
+ print("3. Encoding protein...")
+ prot_result = obm.encode("MKTVRQERLKSIVRILERSKEPVSG", Modality.PROTEIN)
+ print(f" Protein embedding: {len(prot_result.vector)} dims")
+
+ # Cross-modal similarity
+ print("\n4. Cross-modal similarity:")
+ sim = obm.similarity(text_result, mol_result)
+ print(f" Text-Molecule similarity: {sim:.3f}")
+
+ print("\n✅ Full OBM Encoder works!")
+ print(obm.get_encoder_info())
+ return True
+
+ except Exception as e:
+ print(f"❌ Error: {e}")
+ import traceback
+ traceback.print_exc()
+ return False
+
+
+def main():
+ """Run verification tests."""
+ print("="*60)
+ print("🧬 BioFlow Phase 2 Verification")
+ print("="*60)
+
+ full_mode = "--full" in sys.argv
+
+ if full_mode:
+ print("Running FULL tests (with model downloads)")
+ else:
+ print("Running QUICK tests (no model downloads)")
+ print("Add --full flag for complete testing")
+
+ results = {}
+
+ # Always run
+ results["Registry"] = test_registry()
+ results["RDKit"] = test_rdkit_fallback()
+ results["DeepPurpose"] = test_deeppurpose_predictor()
+ results["Qdrant"] = test_qdrant_retriever()
+
+ # Only in full mode
+ if full_mode:
+ results["OBMEncoder"] = test_full_obm_encoder()
+
+ # Summary
+ print("\n" + "="*60)
+ print("📊 VERIFICATION SUMMARY")
+ print("="*60)
+
+ for test, passed in results.items():
+ status = "✅ PASS" if passed else "❌ FAIL"
+ print(f" {test}: {status}")
+
+ all_passed = all(results.values())
+ print("\n" + ("✅ All tests passed!" if all_passed else "⚠️ Some tests failed"))
+
+ return 0 if all_passed else 1
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/scripts/verify_phase3.py b/scripts/verify_phase3.py
new file mode 100644
index 0000000000000000000000000000000000000000..5806d7021757966fe9a177836baae5d8661dcd3a
--- /dev/null
+++ b/scripts/verify_phase3.py
@@ -0,0 +1,351 @@
+"""
+Phase 3 Verification: Unified Workflow
+========================================
+
+Tests the complete discovery pipeline end-to-end:
+1. Ingest sample data into Qdrant
+2. Run discovery pipeline with query
+3. Verify predictions and traceability
+
+Usage:
+ python scripts/verify_phase3.py
+"""
+
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import logging
+logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+logger = logging.getLogger(__name__)
+
+
+def create_mock_components():
+ """Create mock encoder, retriever, predictor for testing."""
+ from bioflow.core import (
+ BioEncoder, BioRetriever, BioPredictor,
+ Modality, EmbeddingResult, RetrievalResult, PredictionResult
+ )
+ import hashlib
+
+ class MockEncoder(BioEncoder):
+ def encode(self, content, modality):
+ h = hashlib.md5(str(content).encode()).hexdigest()
+ vector = [int(c, 16) / 15.0 for c in h] * 48
+ return EmbeddingResult(vector=vector[:768], modality=modality, dimension=768)
+
+ def encode_auto(self, content):
+ return self.encode(content, Modality.TEXT)
+
+ def batch_encode(self, contents, modality):
+ return [self.encode(c, modality) for c in contents]
+
+ @property
+ def dimension(self): return 768
+
+ class MockRetriever(BioRetriever):
+ def __init__(self, encoder):
+ self.encoder = encoder
+ self._data = {}
+ self._vectors = {}
+ self._id_counter = 0
+
+ def search(self, query, limit=10, filters=None, collection=None, modality=None, **kwargs):
+ if isinstance(query, str):
+ query_vec = self.encoder.encode(query, modality or Modality.TEXT).vector
+ else:
+ query_vec = query
+
+ # Simple cosine similarity
+ import math
+ results = []
+ for id_, (vec, payload) in self._vectors.items():
+ dot = sum(a*b for a, b in zip(query_vec, vec))
+ norm_q = math.sqrt(sum(a*a for a in query_vec))
+ norm_v = math.sqrt(sum(b*b for b in vec))
+ score = dot / (norm_q * norm_v) if norm_q * norm_v > 0 else 0
+
+ results.append(RetrievalResult(
+ id=id_,
+ score=score,
+ content=payload.get("content", ""),
+ modality=Modality(payload.get("modality", "text")),
+ payload=payload
+ ))
+
+ results.sort(key=lambda x: x.score, reverse=True)
+ return results[:limit]
+
+ def ingest(self, content, modality, payload=None, collection=None, id=None):
+ self._id_counter += 1
+ id_ = id or f"item_{self._id_counter}"
+ vec = self.encoder.encode(content, modality).vector
+ full_payload = {"content": content, "modality": modality.value, **(payload or {})}
+ self._vectors[id_] = (vec, full_payload)
+ return id_
+
+ def count(self, collection=None):
+ return len(self._vectors)
+
+ class MockPredictor(BioPredictor):
+ def predict(self, drug, target):
+ import random
+ random.seed(hash(drug + target) % 2**32)
+ score = random.uniform(0.2, 0.9)
+ return PredictionResult(
+ score=score,
+ confidence=0.7,
+ label="binding" if score > 0.5 else "non-binding"
+ )
+
+ encoder = MockEncoder()
+ retriever = MockRetriever(encoder)
+ predictor = MockPredictor()
+
+ return encoder, retriever, predictor
+
+
+def test_node_execution():
+ """Test individual node execution."""
+ print("\n" + "="*60)
+ print("🧩 TEST 1: Node Execution")
+ print("="*60)
+
+ from bioflow.core.nodes import (
+ EncodeNode, RetrieveNode, PredictNode, FilterNode, TraceabilityNode
+ )
+ from bioflow.core import Modality
+
+ encoder, retriever, predictor = create_mock_components()
+
+ # Test EncodeNode
+ encode_node = EncodeNode("enc", encoder, Modality.SMILES)
+ result = encode_node.execute("CCO")
+ print(f" EncodeNode: vector dim = {len(result.data.vector)}")
+
+ # Test FilterNode
+ filter_node = FilterNode("filter", threshold=0.5, top_k=3)
+ items = [{"score": 0.9}, {"score": 0.4}, {"score": 0.7}, {"score": 0.3}]
+ result = filter_node.execute(items)
+ print(f" FilterNode: {len(items)} items → {len(result.data)} after filtering")
+
+ # Test TraceabilityNode
+ trace_node = TraceabilityNode("trace")
+ items = [{"id": "PMID_12345", "content": "test", "payload": {"pmid": "12345"}}]
+ result = trace_node.execute(items)
+ print(f" TraceabilityNode: Added {result.metadata['with_evidence']} evidence links")
+
+ print("✅ All nodes execute correctly")
+ return True
+
+
+def test_discovery_pipeline():
+ """Test the full discovery pipeline."""
+ print("\n" + "="*60)
+ print("🔬 TEST 2: Discovery Pipeline")
+ print("="*60)
+
+ from bioflow.workflows import DiscoveryPipeline, generate_sample_molecules
+ from bioflow.core import Modality
+
+ encoder, retriever, predictor = create_mock_components()
+
+ # Create pipeline
+ pipeline = DiscoveryPipeline(
+ encoder=encoder,
+ retriever=retriever,
+ predictor=predictor,
+ collection="test_molecules"
+ )
+
+ # Ingest sample data
+ print("\n1. Ingesting sample molecules...")
+ sample_data = generate_sample_molecules()
+
+ for mol in sample_data:
+ retriever.ingest(
+ content=mol["smiles"],
+ modality=Modality.SMILES,
+ payload={"name": mol["name"], **{k: v for k, v in mol.items() if k not in ["smiles", "modality"]}}
+ )
+
+ print(f" Ingested {retriever.count()} molecules")
+
+ # Run discovery
+ print("\n2. Running discovery pipeline...")
+ target_sequence = "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"
+
+ result = pipeline.discover(
+ query="anti-inflammatory compound",
+ target_sequence=target_sequence,
+ limit=5,
+ threshold=0.3,
+ top_k=3
+ )
+
+ print(f"\n Discovery Result:")
+ print(f" • Query: {result.query[:40]}...")
+ print(f" • Candidates retrieved: {len(result.candidates)}")
+ print(f" • Predictions made: {len(result.predictions)}")
+ print(f" • Top hits: {len(result.top_hits)}")
+ print(f" • Execution time: {result.execution_time_ms:.0f}ms")
+
+ print("\n3. Top hits:")
+ for i, hit in enumerate(result.top_hits[:3]):
+ drug = hit.get("drug", "")[:30]
+ score = hit.get("score", 0)
+ evidence = hit.get("evidence_links", {})
+ print(f" {i+1}. {drug}... (score: {score:.3f})")
+ if evidence:
+ print(f" Evidence: {list(evidence.keys())}")
+
+ print("\n✅ Discovery pipeline works!")
+ return True
+
+
+def test_simple_search():
+ """Test simple similarity search."""
+ print("\n" + "="*60)
+ print("🔍 TEST 3: Simple Search")
+ print("="*60)
+
+ from bioflow.workflows import DiscoveryPipeline, generate_sample_abstracts
+ from bioflow.core import Modality
+
+ encoder, retriever, predictor = create_mock_components()
+
+ pipeline = DiscoveryPipeline(
+ encoder=encoder,
+ retriever=retriever,
+ predictor=predictor
+ )
+
+ # Ingest abstracts
+ print("\n1. Ingesting sample abstracts...")
+ for abstract in generate_sample_abstracts():
+ retriever.ingest(
+ content=abstract["content"],
+ modality=Modality.TEXT,
+ payload={k: v for k, v in abstract.items() if k not in ["content", "modality"]}
+ )
+
+ print(f" Ingested {retriever.count()} items")
+
+ # Search
+ print("\n2. Searching for 'EGFR cancer treatment'...")
+ results = pipeline.search(
+ query="EGFR cancer treatment",
+ modality=Modality.TEXT,
+ limit=3
+ )
+
+ print(f"\n Found {len(results)} results:")
+ for r in results:
+ print(f" • Score: {r.score:.3f} | {r.content[:50]}...")
+
+ print("\n✅ Search works!")
+ return True
+
+
+def test_ingestion_utilities():
+ """Test data ingestion utilities."""
+ print("\n" + "="*60)
+ print("📥 TEST 4: Ingestion Utilities")
+ print("="*60)
+
+ from bioflow.workflows.ingestion import (
+ generate_sample_molecules,
+ generate_sample_proteins,
+ generate_sample_abstracts
+ )
+
+ molecules = generate_sample_molecules()
+ proteins = generate_sample_proteins()
+ abstracts = generate_sample_abstracts()
+
+ print(f" • Sample molecules: {len(molecules)}")
+ print(f" - Example: {molecules[0]['name']} ({molecules[0]['smiles'][:20]}...)")
+
+ print(f" • Sample proteins: {len(proteins)}")
+ print(f" - Example: {proteins[0]['name']} ({proteins[0]['sequence'][:20]}...)")
+
+ print(f" • Sample abstracts: {len(abstracts)}")
+ print(f" - Example: {abstracts[0]['title']}")
+
+ print("\n✅ Ingestion utilities work!")
+ return True
+
+
+def test_traceability():
+ """Test evidence linking and traceability."""
+ print("\n" + "="*60)
+ print("🔗 TEST 5: Traceability & Evidence Linking")
+ print("="*60)
+
+ from bioflow.core.nodes import TraceabilityNode
+
+ trace_node = TraceabilityNode("trace")
+
+ # Test with different ID formats
+ test_items = [
+ {"id": "PMID_12345678", "content": "Paper about EGFR", "payload": {}},
+ {"id": "mol_1", "content": "CCO", "payload": {"drugbank_id": "DB00316", "pubchem_id": "702"}},
+ {"id": "prot_1", "content": "MKTVRQ...", "payload": {"uniprot": "P00533"}},
+ ]
+
+ result = trace_node.execute(test_items)
+
+ print(" Evidence links generated:")
+ for item in result.data:
+ print(f" • ID: {item['id']}")
+ links = item.get("evidence_links", {})
+ if links:
+ for source, url in links.items():
+ print(f" → {source}: {url}")
+ else:
+ print(f" → No links (payload missing IDs)")
+
+ print(f"\n Items with evidence: {result.metadata['with_evidence']}/{len(test_items)}")
+ print("\n✅ Traceability works!")
+ return True
+
+
+def main():
+ """Run all Phase 3 verification tests."""
+ print("="*60)
+ print("🧬 BioFlow Phase 3 Verification: Unified Workflow")
+ print("="*60)
+
+ results = {}
+
+ results["Nodes"] = test_node_execution()
+ results["Discovery"] = test_discovery_pipeline()
+ results["Search"] = test_simple_search()
+ results["Ingestion"] = test_ingestion_utilities()
+ results["Traceability"] = test_traceability()
+
+ # Summary
+ print("\n" + "="*60)
+ print("📊 VERIFICATION SUMMARY")
+ print("="*60)
+
+ for test, passed in results.items():
+ status = "✅ PASS" if passed else "❌ FAIL"
+ print(f" {test}: {status}")
+
+ all_passed = all(results.values())
+ print("\n" + ("✅ All Phase 3 tests passed!" if all_passed else "⚠️ Some tests failed"))
+
+ if all_passed:
+ print("\n🎉 The unified workflow is ready!")
+ print(" You can now:")
+ print(" • Ingest molecules, proteins, and literature")
+ print(" • Run cross-modal similarity search")
+ print(" • Predict drug-target interactions")
+ print(" • Trace results back to sources")
+
+ return 0 if all_passed else 1
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..36b992f80a0a284662ac3059731c6241ce07d193
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,8 @@
+from setuptools import setup, find_packages
+
+setup(
+ name='openbiomed',
+ version='0.1.0',
+ packages=find_packages(include=["openbiomed", "openbiomed.*"]),
+ install_requires=[],
+)
\ No newline at end of file
diff --git a/third_party/.placeholder b/third_party/.placeholder
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ui/app/api/discovery/route.ts b/ui/app/api/discovery/route.ts
deleted file mode 100644
index 0ba535c39b4944fc5ef15035fae6a74bfce6ec07..0000000000000000000000000000000000000000
--- a/ui/app/api/discovery/route.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import { NextResponse } from 'next/server';
-
-export async function POST(request: Request) {
- const body = await request.json();
- const { query } = body;
-
- console.info("Starting discovery for:", query);
-
- // Here you would typically call your Python backend
- // e.g., await fetch('http://localhost:8000/api/discovery', { ... })
-
- // Mock response
- return NextResponse.json({
- success: true,
- jobId: "job_" + Date.now(),
- status: "processing",
- message: "Pipeline started successfully"
- });
-}
diff --git a/ui/app/api/molecules/route.ts b/ui/app/api/molecules/route.ts
deleted file mode 100644
index cc44d5adaaf4b2c3c83e9b1e7e99d61baa90b762..0000000000000000000000000000000000000000
--- a/ui/app/api/molecules/route.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { NextResponse } from 'next/server';
-
-import { molecules } from '../_mock/molecules';
-
-export async function GET() {
- return NextResponse.json(molecules);
-}
diff --git a/ui/app/api/proteins/route.ts b/ui/app/api/proteins/route.ts
deleted file mode 100644
index 6985cd64f8e1922030f6b00d63d3f1f5cf76ca76..0000000000000000000000000000000000000000
--- a/ui/app/api/proteins/route.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { NextResponse } from 'next/server';
-
-import { proteins } from '../_mock/proteins';
-
-export async function GET() {
- return NextResponse.json(proteins);
-}
diff --git a/ui/lib/api-utils.ts b/ui/lib/api-utils.ts
deleted file mode 100644
index 2027029525eb3d1ac0f57ac3e73ed4d962d28bd7..0000000000000000000000000000000000000000
--- a/ui/lib/api-utils.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-export function getApiUrl() {
- if (typeof window !== "undefined") return ""; // browser should use relative url
- if (process.env.BACKEND_URL) return `https://${process.env.BACKEND_URL}`; // SSR should use vercel url
- return `http://localhost:${process.env.PORT ?? 3000}`; // dev SSR should use localhost
-}
diff --git a/ui/lib/data-service.ts b/ui/lib/data-service.ts
deleted file mode 100644
index 8e3ba87f85eec2172e4c6143a96e48fabe4caabf..0000000000000000000000000000000000000000
--- a/ui/lib/data-service.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import { DataResponse } from "@/types/data";
-
-export async function getData(): Promise {
- // Mock data simulation - replacing actual database/API call
- const datasets = [
- { name: "DrugBank Compounds", type: "Molecules", count: "12,450", size: "45.2 MB", updated: "2024-01-15" },
- { name: "ChEMBL Kinase Inhibitors", type: "Molecules", count: "8,234", size: "32.1 MB", updated: "2024-01-10" },
- { name: "Custom Protein Targets", type: "Proteins", count: "1,245", size: "78.5 MB", updated: "2024-01-08" },
- ];
-
- const stats = {
- datasets: 5,
- molecules: "24.5K",
- proteins: "1.2K",
- storage: "156 MB",
- };
-
- return Promise.resolve({ datasets, stats });
-}
diff --git a/ui/lib/explorer-service.ts b/ui/lib/explorer-service.ts
deleted file mode 100644
index 13bfb5a6a42920fa51bbc7488900993a0f8dc412..0000000000000000000000000000000000000000
--- a/ui/lib/explorer-service.ts
+++ /dev/null
@@ -1,52 +0,0 @@
-import { ExplorerRequestSchema } from "@/schemas/explorer";
-import { DataPoint,ExplorerResponse } from "@/types/explorer";
-
-const clusters = [
- { cx: 2, cy: 3, color: "var(--color-chart-1)" },
- { cx: -2, cy: -1, color: "var(--color-chart-2)" },
- { cx: 4, cy: -2, color: "var(--color-chart-3)" },
- { cx: -1, cy: 4, color: "var(--color-chart-4)" },
-];
-
-export async function getExplorerPoints(
- dataset?: string,
- view?: string,
- colorBy?: string
-): Promise {
- // Validate params using the schema to ensure defaults are applied if needed, even for internal calls
- // Note: safeParse is synchronous, but we can treat this as an async service
- const result = ExplorerRequestSchema.safeParse({
- dataset,
- view,
- colorBy,
- });
-
- if (!result.success) {
- // Return empty/default response or throw type-safe error
- // For now, adhering to existing behavior, we can just throw or fallback
- throw new Error("Invalid parameters");
- }
-
-
- // TODO: replace mock generation with backend embeddings
- const points: DataPoint[] = [];
- for (let i = 0; i < 200; i++) {
- const cluster = clusters[Math.floor(i / 50)];
- points.push({
- x: cluster.cx + (Math.random() - 0.5) * 2,
- y: cluster.cy + (Math.random() - 0.5) * 2,
- z: Math.random() * 100,
- color: cluster.color,
- name: `Mol_${i}`,
- affinity: Math.random() * 100,
- });
- }
-
- const metrics = {
- activeMolecules: 12450,
- clusters: 4,
- avgConfidence: 0.89,
- };
-
- return Promise.resolve({ points, metrics });
-}
diff --git a/ui/lib/get-strict-context.tsx b/ui/lib/get-strict-context.tsx
deleted file mode 100644
index be139dcd5a5ff35a2d02a80f911998c7cf06d3ab..0000000000000000000000000000000000000000
--- a/ui/lib/get-strict-context.tsx
+++ /dev/null
@@ -1,36 +0,0 @@
-import * as React from 'react';
-
-function getStrictContext(
- name?: string,
-): readonly [
- ({
- value,
- children,
- }: {
- value: T;
- children?: React.ReactNode;
- }) => React.JSX.Element,
- () => T,
-] {
- const Context = React.createContext(undefined);
-
- const Provider = ({
- value,
- children,
- }: {
- value: T;
- children?: React.ReactNode;
- }) => {children};
-
- const useSafeContext = () => {
- const ctx = React.useContext(Context);
- if (ctx === undefined) {
- throw new Error(`useContext must be used within ${name ?? 'a Provider'}`);
- }
- return ctx;
- };
-
- return [Provider, useSafeContext] as const;
-}
-
-export { getStrictContext };
diff --git a/ui/lib/utils.ts b/ui/lib/utils.ts
deleted file mode 100644
index 5e205c6b807d9491f1b2148838d3fcb7b1d0bf2b..0000000000000000000000000000000000000000
--- a/ui/lib/utils.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { type ClassValue,clsx } from "clsx"
-import { twMerge } from "tailwind-merge"
-
-export function cn(...inputs: ClassValue[]) {
- return twMerge(clsx(inputs))
-}
diff --git a/ui/lib/visualization-api.ts b/ui/lib/visualization-api.ts
deleted file mode 100644
index 9b0509f8d701049aab88017c54bd07824ff7b8eb..0000000000000000000000000000000000000000
--- a/ui/lib/visualization-api.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-import type { Molecule, Protein } from './visualization-types';
-
-const API_BASE = '/api';
-
-// Generic fetch helper with error handling
-async function fetchApi(url: string): Promise {
- const response = await fetch(url);
- if (!response.ok) {
- const error = await response.json().catch(() => ({ error: 'Unknown error' }));
- throw new Error(error.error || `HTTP ${response.status}`);
- }
- return response.json();
-}
-
-async function fetchText(url: string): Promise {
- const response = await fetch(url);
- if (!response.ok) {
- const error = await response.json().catch(() => ({ error: 'Unknown error' }));
- throw new Error(error.error || `HTTP ${response.status}`);
- }
- return response.text();
-}
-
-// Molecule API
-export async function getMolecules(): Promise {
- return fetchApi(`${API_BASE}/molecules`);
-}
-
-export async function getMolecule(id: string): Promise {
- return fetchApi(`${API_BASE}/molecules/${id}`);
-}
-
-export async function getMoleculeSDF(id: string): Promise {
- return fetchText(`${API_BASE}/molecules/${id}/sdf`);
-}
-
-// Protein API
-export async function getProteins(): Promise {
- return fetchApi(`${API_BASE}/proteins`);
-}
-
-export async function getProtein(id: string): Promise {
- return fetchApi(`${API_BASE}/proteins/${id}`);
-}
-
-export async function getProteinPDB(id: string): Promise {
- return fetchText(`${API_BASE}/proteins/${id}/pdb`);
-}
-
-// URL builders for direct links
-export function getMoleculeSdfUrl(id: string): string {
- return `${API_BASE}/molecules/${id}/sdf`;
-}
-
-export function getProteinPdbUrl(id: string): string {
- return `${API_BASE}/proteins/${id}/pdb`;
-}
diff --git a/ui/lib/visualization-types.ts b/ui/lib/visualization-types.ts
deleted file mode 100644
index f07b0559ddce35a41e9435454d690336655850ce..0000000000000000000000000000000000000000
--- a/ui/lib/visualization-types.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-// Molecule types
-export type Molecule = {
- id: string;
- name: string;
- smiles: string;
- pubchemCid: number;
- description?: string;
-};
-
-// Protein types
-export type Protein = {
- id: string;
- pdbId: string;
- name: string;
- description?: string;
-};
-
-// Viewer representation types
-export type MoleculeRepresentation = 'stick' | 'sphere' | 'line' | 'cartoon';
-export type ProteinRepresentation = 'cartoon' | 'ball-and-stick' | 'surface' | 'ribbon';
+