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ee96220 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 | """PeptiVerse binding-affinity adapter for TD3B.
This module implements the pooled target-sequence/binder-SMILES model published
in ChatterjeeLab/PeptiVerse without loading PeptiVerse's unrelated predictors.
"""
import logging
from pathlib import Path
from typing import Dict, List, Optional
import torch
import torch.nn as nn
logger = logging.getLogger(__name__)
DEFAULT_REPO_ID = "ChatterjeeLab/PeptiVerse"
DEFAULT_CHECKPOINT_FILE = (
"training_classifiers/binding_affinity/"
"chemberta_smiles_pooled/best_model.pt"
)
DEFAULT_ESM_MODEL = "facebook/esm2_t33_650M_UR50D"
DEFAULT_CHEMBERTA_MODEL = "DeepChem/ChemBERTa-77M-MLM"
class PeptiVersePooledAffinityModel(nn.Module):
"""PeptiVerse's bidirectional cross-attention affinity head."""
def __init__(
self,
target_dim: int,
binder_dim: int,
hidden_dim: int,
n_heads: int,
n_layers: int,
dropout: float,
) -> None:
super().__init__()
self.t_proj = nn.Sequential(
nn.Linear(target_dim, hidden_dim), nn.LayerNorm(hidden_dim)
)
self.b_proj = nn.Sequential(
nn.Linear(binder_dim, hidden_dim), nn.LayerNorm(hidden_dim)
)
self.layers = nn.ModuleList()
for _ in range(n_layers):
self.layers.append(
nn.ModuleDict(
{
"attn_tb": nn.MultiheadAttention(
hidden_dim, n_heads, dropout=dropout
),
"attn_bt": nn.MultiheadAttention(
hidden_dim, n_heads, dropout=dropout
),
"n1t": nn.LayerNorm(hidden_dim),
"n2t": nn.LayerNorm(hidden_dim),
"n1b": nn.LayerNorm(hidden_dim),
"n2b": nn.LayerNorm(hidden_dim),
"fft": nn.Sequential(
nn.Linear(hidden_dim, 4 * hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(4 * hidden_dim, hidden_dim),
),
"ffb": nn.Sequential(
nn.Linear(hidden_dim, 4 * hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(4 * hidden_dim, hidden_dim),
),
}
)
)
self.shared = nn.Sequential(
nn.Linear(2 * hidden_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
)
self.reg = nn.Linear(hidden_dim, 1)
self.cls = nn.Linear(hidden_dim, 3)
def forward(self, target: torch.Tensor, binder: torch.Tensor):
target = self.t_proj(target).unsqueeze(0)
binder = self.b_proj(binder).unsqueeze(0)
for layer in self.layers:
target_attn, _ = layer["attn_tb"](target, binder, binder)
target = layer["n1t"]((target + target_attn).transpose(0, 1)).transpose(0, 1)
target = layer["n2t"](
(target + layer["fft"](target)).transpose(0, 1)
).transpose(0, 1)
binder_attn, _ = layer["attn_bt"](binder, target, target)
binder = layer["n1b"]((binder + binder_attn).transpose(0, 1)).transpose(0, 1)
binder = layer["n2b"](
(binder + layer["ffb"](binder)).transpose(0, 1)
).transpose(0, 1)
hidden = self.shared(torch.cat([target[0], binder[0]], dim=-1))
return self.reg(hidden).squeeze(-1), self.cls(hidden)
class PeptiVerseBindingAffinity:
"""Score target/peptide-SMILES pairs with PeptiVerse's pK regressor."""
backend_name = "peptiverse"
def __init__(
self,
device=None,
checkpoint_path: Optional[str] = None,
repo_id: str = DEFAULT_REPO_ID,
revision: Optional[str] = None,
cache_dir: Optional[str] = None,
local_files_only: bool = False,
esm_name: str = DEFAULT_ESM_MODEL,
chemberta_name: str = DEFAULT_CHEMBERTA_MODEL,
batch_size: int = 32,
max_protein_length: int = 1022,
max_smiles_length: int = 512,
) -> None:
from transformers import AutoModel, AutoTokenizer, EsmModel, EsmTokenizer
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
) if device is None else torch.device(device)
self.batch_size = max(1, int(batch_size))
self.max_protein_length = max_protein_length
self.max_smiles_length = max_smiles_length
resolved_checkpoint = self._resolve_checkpoint(
checkpoint_path=checkpoint_path,
repo_id=repo_id,
revision=revision,
cache_dir=cache_dir,
local_files_only=local_files_only,
)
checkpoint = torch.load(
resolved_checkpoint, map_location=self.device, weights_only=False
)
if checkpoint.get("mode") != "pooled":
raise ValueError(
f"Expected a pooled PeptiVerse checkpoint, got {checkpoint.get('mode')!r}"
)
state_dict = checkpoint["state_dict"]
params = checkpoint.get("best_params", {})
model = PeptiVersePooledAffinityModel(
target_dim=int(state_dict["t_proj.0.weight"].shape[1]),
binder_dim=int(state_dict["b_proj.0.weight"].shape[1]),
hidden_dim=int(params.get("hidden_dim", state_dict["t_proj.0.weight"].shape[0])),
n_heads=int(params.get("n_heads", 4)),
n_layers=int(params.get("n_layers", self._infer_layers(state_dict))),
dropout=float(params.get("dropout", 0.0)),
)
model.load_state_dict(state_dict, strict=True)
self.model = model.to(self.device).eval()
model_kwargs = {
"cache_dir": cache_dir,
"local_files_only": local_files_only,
}
self.target_tokenizer = EsmTokenizer.from_pretrained(esm_name, **model_kwargs)
self.target_encoder = EsmModel.from_pretrained(
esm_name, add_pooling_layer=False, **model_kwargs
).to(self.device).eval()
self.binder_tokenizer = AutoTokenizer.from_pretrained(
chemberta_name, **model_kwargs
)
self.binder_encoder = AutoModel.from_pretrained(
chemberta_name, **model_kwargs
).to(self.device).eval()
self.target_cache: Dict[str, torch.Tensor] = {}
logger.info("Loaded PeptiVerse affinity checkpoint: %s", resolved_checkpoint)
@staticmethod
def _resolve_checkpoint(
checkpoint_path: Optional[str],
repo_id: str,
revision: Optional[str],
cache_dir: Optional[str],
local_files_only: bool,
) -> str:
if checkpoint_path is not None:
path = Path(checkpoint_path).expanduser()
if not path.is_file():
raise FileNotFoundError(f"PeptiVerse checkpoint not found: {path}")
return str(path)
from huggingface_hub import hf_hub_download
return hf_hub_download(
repo_id=repo_id,
filename=DEFAULT_CHECKPOINT_FILE,
revision=revision,
cache_dir=cache_dir,
local_files_only=local_files_only,
)
@staticmethod
def _infer_layers(state_dict) -> int:
layer_ids = {
int(key.split(".")[1])
for key in state_dict
if key.startswith("layers.")
}
return max(layer_ids) + 1
@staticmethod
def _special_token_ids(tokenizer) -> List[int]:
values = [
getattr(tokenizer, f"{name}_token_id", None)
for name in ("pad", "cls", "sep", "bos", "eos", "mask")
]
return sorted({int(value) for value in values if value is not None})
@torch.no_grad()
def _pool(self, texts, tokenizer, encoder, max_length: int) -> torch.Tensor:
tokens = tokenizer(
list(texts),
return_tensors="pt",
padding=True,
truncation=True,
max_length=max_length,
)
tokens = {name: value.to(self.device) for name, value in tokens.items()}
attention_mask = tokens.get(
"attention_mask", torch.ones_like(tokens["input_ids"])
).bool()
valid_mask = attention_mask
special_ids = self._special_token_ids(tokenizer)
if special_ids:
special = torch.tensor(special_ids, device=self.device)
valid_mask = valid_mask & ~torch.isin(tokens["input_ids"], special)
hidden = encoder(**tokens).last_hidden_state
weights = valid_mask.unsqueeze(-1).to(hidden.dtype)
return (hidden * weights).sum(dim=1) / weights.sum(dim=1).clamp_min(1.0)
def get_protein_embedding(self, prot_seq: str) -> torch.Tensor:
prot_seq = prot_seq.strip()
if prot_seq not in self.target_cache:
self.target_cache[prot_seq] = self._pool(
[prot_seq],
self.target_tokenizer,
self.target_encoder,
self.max_protein_length,
)
return self.target_cache[prot_seq]
@torch.no_grad()
def forward(self, input_seqs, prot_seq: str):
input_seqs = list(input_seqs)
if not input_seqs:
return []
target = self.get_protein_embedding(prot_seq)
scores = []
for start in range(0, len(input_seqs), self.batch_size):
batch = input_seqs[start:start + self.batch_size]
binder = self._pool(
batch,
self.binder_tokenizer,
self.binder_encoder,
self.max_smiles_length,
)
affinity, _ = self.model(target.expand(len(batch), -1), binder)
scores.extend(affinity.detach().cpu().tolist())
return scores
def __call__(self, input_seqs, prot_seq: str):
return self.forward(input_seqs, prot_seq)
def clear_cache(self) -> None:
self.target_cache.clear()
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