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import copy
import random
import torch
import numpy as np
from torch_geometric.data import Data, Batch
from torch_scatter import scatter_sum
# from torch_geometric.loader import DataLoader
from torch.utils.data import Dataset
FOLLOW_BATCH = ['protein_element', 'ligand_context_element', 'pos_real', 'pos_fake']
class ProteinLigandData(object):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@staticmethod
def from_protein_ligand_dicts(protein_dict=None, ligand_dict=None, **kwargs):
instance = ProteinLigandData(**kwargs)
if protein_dict is not None:
for key, item in protein_dict.items():
instance['protein_' + key] = item
if ligand_dict is not None:
for key, item in ligand_dict.items():
instance['ligand_' + key] = item
# instance['ligand_nbh_list'] = {i.item():[j.item() for k, j in enumerate(instance.ligand_bond_index[1]) if instance.ligand_bond_index[0, k].item() == i] for i in instance.ligand_bond_index[0]}
return instance
def batch_from_data_list(data_list):
return Batch.from_data_list(data_list, follow_batch=['ligand_element', 'protein_element'])
def torchify_dict(data):
output = {}
for k, v in data.items():
if isinstance(v, np.ndarray):
output[k] = torch.from_numpy(v)
else:
output[k] = v
return output
def collate_mols(mol_dicts):
data_batch = {}
batch_size = len(mol_dicts)
for key in ['protein_pos', 'protein_atom_feature', 'ligand_pos', 'ligand_atom_feature',
'protein_edit_residue', 'amino_acid', 'res_idx', 'residue_natoms', 'protein_atom_to_aa_type']:
data_batch[key] = torch.cat([mol_dict[key] for mol_dict in mol_dicts], dim=0)
# residue pos
data_batch['residue_pos'] = \
torch.cat([torch.cat([mol_dict[key] for mol_dict in mol_dicts], dim=0).unsqueeze(0) for key in ['pos_N', 'pos_CA', 'pos_C', 'pos_O']], dim=0).permute(1,0,2)
# random mask residues for the second stage (one residue per protein)
tmp = []
for mol_dict in mol_dicts:
ind = torch.multinomial(mol_dict['protein_edit_residue'].float(), 1)
selected = torch.zeros_like(mol_dict['protein_edit_residue'], dtype=bool)
selected[ind] = 1
tmp.append(selected)
data_batch['random_mask_residue'] = torch.cat(tmp, dim=0)
# remove side chains for the masked atoms
num_residues = len(data_batch['amino_acid'])
data_batch['atom2residue'] = torch.repeat_interleave(torch.arange(num_residues), data_batch['residue_natoms'])
index1 = torch.arange(len(data_batch['amino_acid']))[data_batch['random_mask_residue']]
index2 = torch.arange(len(data_batch['amino_acid']))[data_batch['protein_edit_residue']]
for key in ['protein_pos', 'protein_atom_feature']:
tmp1, tmp2 = [], []
for k in range(num_residues):
mask = data_batch['atom2residue'] == k
if k in index1:
tmp1.append(data_batch[key][mask][:4])
else:
tmp1.append(data_batch[key][mask])
if k in index2:
tmp2.append(data_batch[key][mask][:4])
else:
tmp2.append(data_batch[key][mask])
data_batch[key] = torch.cat(tmp1, dim=0)
data_batch[key + '_backbone'] = torch.cat(tmp2, dim=0)
data_batch['residue_natoms'][data_batch['random_mask_residue']] = 4
data_batch['atom2residue'] = torch.repeat_interleave(torch.arange(len(data_batch['residue_natoms'])), data_batch['residue_natoms'])
# follow batch
for key in ['ligand_atom_feature', 'amino_acid']:
repeats = torch.tensor([len(mol_dict[key]) for mol_dict in mol_dicts])
if key == 'amino_acid':
data_batch['amino_acid_batch'] = torch.repeat_interleave(torch.arange(batch_size), repeats)
else:
data_batch['ligand_atom_batch'] = torch.repeat_interleave(torch.arange(batch_size), repeats)
repeats = scatter_sum(data_batch['residue_natoms'], data_batch['amino_acid_batch'], dim=0)
data_batch['protein_atom_batch'] = torch.repeat_interleave(torch.arange(batch_size), repeats)
# backbone protein for the first stage
data_batch['residue_natoms_backbone'] = copy.deepcopy(data_batch['residue_natoms'])
data_batch['residue_natoms_backbone'][data_batch['protein_edit_residue']] = 4
repeats = scatter_sum(data_batch['residue_natoms_backbone'], data_batch['amino_acid_batch'], dim=0)
data_batch['protein_atom_batch_backbone'] = torch.repeat_interleave(torch.arange(batch_size), repeats)
data_batch['atom2residue_backbone'] = torch.repeat_interleave(torch.arange(len(data_batch['residue_natoms_backbone'])), data_batch['residue_natoms_backbone'])
data_batch['protein_edit_atom'] = torch.repeat_interleave(data_batch['protein_edit_residue'], data_batch['residue_natoms'], dim=0)
data_batch['protein_edit_atom_backbone'] = torch.repeat_interleave(data_batch['protein_edit_residue'], data_batch['residue_natoms_backbone'], dim=0)
data_batch['random_mask_atom'] = torch.repeat_interleave(data_batch['random_mask_residue'], data_batch['residue_natoms'], dim=0)
data_batch['edit_sidechain'] = copy.deepcopy(data_batch['protein_edit_atom'])
data_batch['edit_backbone'] = copy.deepcopy(data_batch['protein_edit_atom'])
index = torch.arange(len(data_batch['amino_acid']))[data_batch['protein_edit_residue']]
for k in range(num_residues):
mask = data_batch['atom2residue'] == k
if k in index:
data_mask1, data_mask2 = data_batch['edit_sidechain'][mask], data_batch['edit_backbone'][mask]
data_mask1[:4], data_mask2[4:] = 0, 0
data_batch['edit_sidechain'][mask] = data_mask1
data_batch['edit_backbone'][mask] = data_mask2
return data_batch
def collate_mols_block(mol_dicts, batch_converter):
data_batch = {}
batch_size = len(mol_dicts)
for key in ['protein_pos', 'protein_atom_feature', 'protein_atom_name', 'protein_edit_residue', 'amino_acid', 'residue_natoms', 'protein_atom_to_aa_type', 'res_idx', 'ligand_element', 'ligand_bond_type']:
data_batch[key] = torch.cat([mol_dict[key] for mol_dict in mol_dicts], dim=0)
edge_num = torch.tensor([len(mol_dict['ligand_bond_type']) for mol_dict in mol_dicts])
ligand_atom_num = torch.tensor([len(mol_dict['ligand_element']) for mol_dict in mol_dicts])
data_batch['edge_batch'] = torch.repeat_interleave(torch.arange(batch_size), edge_num)
data_batch['ligand_batch'] = torch.repeat_interleave(torch.arange(batch_size), ligand_atom_num)
data_batch['ligand_bond_index'] = torch.cat([mol_dict['ligand_bond_index'] for mol_dict in mol_dicts], dim=1)
# protein backbone pos
data_batch['backbone_pos'] = torch.cat([torch.cat([mol_dict[key] for mol_dict in mol_dicts], dim=0).unsqueeze(0) for key in ['pos_N', 'pos_CA', 'pos_C', 'pos_O']], dim=0).permute(1, 0, 2)
# protein residue/feature for residue level encoding
num_residues = len(data_batch['amino_acid'])
data_batch['amino_acid_processed'] = copy.deepcopy(data_batch['amino_acid'])
data_batch['amino_acid_processed'][data_batch['protein_edit_residue']] = 0
data_batch['atom2residue'] = torch.repeat_interleave(torch.arange(num_residues), data_batch['residue_natoms'])
data_batch['residue_pos'] = torch.zeros(num_residues, 14, 3).to(data_batch['amino_acid'].device)
# data_batch['residue_feat'] = torch.zeros(num_residues, 14, 38).to(data_batch['amino_acid'].device)
index = torch.arange(num_residues)[data_batch['protein_edit_residue']]
for k in range(num_residues):
mask = data_batch['atom2residue'] == k
data_batch['residue_pos'][k][:min(data_batch['residue_natoms'][k].item(), 14)] = data_batch['protein_pos'][mask][:min(data_batch['residue_natoms'][k].item(), 14)]
'''
if k in index:
data_batch['residue_feat'][k][:4] = data_batch['protein_atom_feature'][mask][:4]
else:
data_batch['residue_feat'][k][:data_batch['residue_natoms'][k]] = data_batch['protein_atom_feature'][mask]
'''
# residue, ligand, protein atom follow batch
repeats = torch.tensor([len(mol_dict['amino_acid']) for mol_dict in mol_dicts])
data_batch['amino_acid_batch'] = torch.repeat_interleave(torch.arange(batch_size), repeats)
# ligand pos feat
data_batch['ligand_natoms'] = torch.tensor([len(mol_dict['ligand_pos']) for mol_dict in mol_dicts])
max_ligand_atoms = max([len(mol_dict['ligand_pos']) for mol_dict in mol_dicts])
data_batch['ligand_pos'] = torch.zeros(batch_size, max_ligand_atoms, 3).to(data_batch['amino_acid'].device)
data_batch['ligand_feat'] = torch.zeros(batch_size, max_ligand_atoms, 15).to(data_batch['amino_acid'].device)
data_batch['ligand_mask'] = torch.zeros(batch_size, max_ligand_atoms).to(data_batch['amino_acid'].device)
for b in range(batch_size):
data_batch['ligand_pos'][b][:data_batch['ligand_natoms'][b]] = mol_dicts[b]['ligand_pos']
data_batch['ligand_feat'][b][:data_batch['ligand_natoms'][b]] = mol_dicts[b]['ligand_atom_feature']
data_batch['ligand_mask'][b, :data_batch['ligand_natoms'][b]] = 1
data_batch['edit_residue_num'] = torch.tensor([mol_dict['protein_edit_residue'].sum() for mol_dict in mol_dicts]).to(data_batch['amino_acid'].device)
data_batch['seq'] = [('', mol_dict['seq']) for mol_dict in mol_dicts]
_, _, data_batch['seq'] = batch_converter(data_batch['seq'])
mask_id = 32
data_batch['full_seq_mask'] = torch.zeros_like(data_batch['seq']).bool()
data_batch['r10_mask'] = torch.zeros_like(data_batch['seq']).bool()
for b in range(batch_size):
data_batch['seq'][b][mol_dicts[b]['full_seq_idx']+1] = mask_id
data_batch['full_seq_mask'][b][mol_dicts[b]['full_seq_idx']+1] = True
data_batch['r10_mask'][b][mol_dicts[b]['r10_idx'] + 1] = True
data_batch['protein_filename'] = [mol_dict['whole_protein_name'] for mol_dict in mol_dicts]
data_batch['pocket_filename'] = [mol_dict['protein_filename'] for mol_dict in mol_dicts]
data_batch['ligand_filename'] = [mol_dict['ligand_filename'] for mol_dict in mol_dicts]
return data_batch
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