File size: 9,438 Bytes
07fcdfe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | """Drug molecular encoder for structure-based drug representation.
Provides two encoding strategies:
1. Morgan fingerprints (ECFP4) → MLP projection [MVI, no training needed]
2. MPNN graph encoder (pre-trained, frozen) [Phase 2]
The drug embedding bridges the gap between molecular structure and gene-level
intervention effects, enabling the model to differentiate drugs that target
the same genes but have different downstream transcriptional signatures.
Usage
-----
encoder = DrugEncoder(encoding="morgan", emb_dim=128)
smiles_list = ["CC(=O)Oc1ccccc1C(=O)O", "CN1C=NC2=C1C(=O)N(C(=O)N2C)C"]
drug_emb = encoder(smiles_list) # [n_drugs, emb_dim]
References
----------
- Morgan fingerprints: Rogers & Hahn (2010) "Extended-Connectivity Fingerprints"
- MPNN: Gilmer et al. (2018) "Neural Message Passing for Quantum Chemistry"
"""
from __future__ import annotations
import logging
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
class DrugEncoder(nn.Module):
"""Encode drug SMILES strings into continuous embeddings.
Parameters
----------
encoding : str
"morgan" for ECFP4 fingerprints (MVI, no training needed).
"mpnn" for graph neural network (requires RDKit + pre-trained weights).
emb_dim : int
Output embedding dimension. Default 128.
morgan_radius : int
Radius for Morgan fingerprint computation. Default 2.
morgan_nbits : int
Number of bits in Morgan fingerprint. Default 2048.
freeze : bool
If True, freeze all encoder parameters (recommended for pre-trained MPNN).
"""
def __init__(
self,
encoding: str = "morgan",
emb_dim: int = 128,
morgan_radius: int = 2,
morgan_nbits: int = 2048,
freeze: bool = True,
) -> None:
super().__init__()
self.encoding = encoding
self.emb_dim = emb_dim
self.freeze = freeze
if encoding == "morgan":
self._init_morgan(morgan_nbits, morgan_radius, emb_dim)
elif encoding == "mpnn":
self._init_mpnn(emb_dim)
else:
raise ValueError(f"Unknown encoding: {encoding}. Choose 'morgan' or 'mpnn'.")
if freeze:
for param in self.parameters():
param.requires_grad = False
logger.info("DrugEncoder: all parameters frozen.")
# ------------------------------------------------------------------
# Morgan fingerprint backend
# ------------------------------------------------------------------
def _init_morgan(self, nbits: int, radius: int, emb_dim: int) -> None:
"""Initialize Morgan fingerprint encoder with MLP projection."""
self.fingerprint_dim = nbits
self.projection = nn.Sequential(
nn.Linear(nbits, max(nbits // 2, emb_dim)),
nn.LayerNorm(max(nbits // 2, emb_dim)),
nn.GELU(),
nn.Linear(max(nbits // 2, emb_dim), emb_dim),
)
logger.info(
"DrugEncoder: Morgan fingerprint (radius=%d, nbits=%d) → MLP(%d→%d→%d)",
radius, nbits, nbits, max(nbits // 2, emb_dim), emb_dim,
)
# ------------------------------------------------------------------
# MPNN backend (placeholder for Phase 2)
# ------------------------------------------------------------------
def _init_mpnn(self, emb_dim: int) -> None:
"""Initialize MPNN graph encoder (placeholder for pre-trained weights)."""
# Placeholder — will be replaced with actual MPNN in Phase 2
self.fingerprint_dim = 2048 # fallback
self.projection = nn.Sequential(
nn.Linear(2048, 512),
nn.GELU(),
nn.Linear(512, emb_dim),
)
logger.warning("DrugEncoder: MPNN not yet implemented, using fallback MLP on 2048-dim input.")
# ------------------------------------------------------------------
# Forward
# ------------------------------------------------------------------
def forward(self, smiles_list: list[str]) -> torch.Tensor:
"""Encode a list of SMILES strings to drug embeddings.
Parameters
----------
smiles_list : list of str
SMILES strings for each drug molecule.
Returns
-------
emb : [n_drugs, emb_dim] float tensor
Drug embeddings on the same device as the module's parameters.
"""
if len(smiles_list) == 0:
device = next(self.parameters()).device
return torch.empty(0, self.emb_dim, device=device)
if self.encoding == "morgan":
fps = self._compute_morgan_fps(smiles_list)
fps_tensor = torch.as_tensor(fps, dtype=torch.float32)
# Move to same device as model parameters
device = next(self.parameters()).device
fps_tensor = fps_tensor.to(device)
emb = self.projection(fps_tensor)
elif self.encoding == "mpnn":
fps = self._compute_morgan_fps(smiles_list) # fallback
fps_tensor = torch.as_tensor(fps, dtype=torch.float32)
device = next(self.parameters()).device
fps_tensor = fps_tensor.to(device)
emb = self.projection(fps_tensor)
return emb
# ------------------------------------------------------------------
# Morgan fingerprint computation
# ------------------------------------------------------------------
def _compute_morgan_fps(self, smiles_list: list[str]) -> np.ndarray:
"""Compute ECFP4 Morgan fingerprints for a list of SMILES.
Parameters
----------
smiles_list : list of str
Returns
-------
fps : [n_drugs, morgan_nbits] binary array
"""
try:
from rdkit import Chem
from rdkit.Chem import AllChem
except ImportError:
raise ImportError(
"RDKit is required for Morgan fingerprint computation. "
"Install with: conda install -c conda-forge rdkit"
)
nbits = self.fingerprint_dim
fps = np.zeros((len(smiles_list), nbits), dtype=np.float32)
for i, smiles in enumerate(smiles_list):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
logger.warning("Failed to parse SMILES: %s", smiles)
continue
fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=nbits)
fps[i] = np.array(list(fp), dtype=np.float32)
return fps
# ------------------------------------------------------------------
# Utility: compute similarity between two drug embeddings
# ------------------------------------------------------------------
@torch.no_grad()
def similarity(self, emb_a: torch.Tensor, emb_b: torch.Tensor) -> torch.Tensor:
"""Compute cosine similarity between drug embeddings.
Parameters
----------
emb_a : [n, emb_dim]
emb_b : [m, emb_dim]
Returns
-------
sim : [n, m] cosine similarity matrix
"""
a_norm = F.normalize(emb_a, p=2, dim=-1)
b_norm = F.normalize(emb_b, p=2, dim=-1)
return torch.mm(a_norm, b_norm.t())
# ---------------------------------------------------------------------------
# Cache for computed embeddings (avoid recomputation)
# ---------------------------------------------------------------------------
class DrugEmbeddingCache:
"""Cache drug embeddings by SMILES to avoid recomputation.
Parameters
----------
encoder : DrugEncoder
cache_path : str, optional
Path to pickle file for persistent caching across sessions.
"""
def __init__(self, encoder: DrugEncoder, cache_path: Optional[str] = None):
self.encoder = encoder
self.cache_path = cache_path
self._cache: dict[str, torch.Tensor] = {}
def get(self, smiles: str) -> torch.Tensor:
"""Get embedding for a single SMILES string (cached)."""
if smiles not in self._cache:
emb = self.encoder([smiles]).squeeze(0).detach().cpu()
self._cache[smiles] = emb
return self._cache[smiles]
def get_batch(self, smiles_list: list[str]) -> torch.Tensor:
"""Get embeddings for a batch of SMILES strings."""
uncached = [s for s in smiles_list if s not in self._cache]
if uncached:
embs = self.encoder(uncached).detach().cpu()
for s, e in zip(uncached, embs):
self._cache[s] = e
return torch.stack([self._cache[s] for s in smiles_list])
def save(self) -> None:
"""Save cache to disk."""
if self.cache_path:
import pickle
with open(self.cache_path, "wb") as f:
pickle.dump({k: v.numpy() for k, v in self._cache.items()}, f)
def load(self) -> None:
"""Load cache from disk."""
if self.cache_path:
import os
import pickle
if os.path.exists(self.cache_path):
with open(self.cache_path, "rb") as f:
raw = pickle.load(f)
device = next(self.encoder.parameters()).device
self._cache = {k: torch.as_tensor(v, device=device) for k, v in raw.items()}
|