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89d9c28 | 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 | #!/usr/bin/env python3
import argparse
import json
from pathlib import Path
import pandas as pd
import requests
import os
import sys
from rdkit import Chem
from rdkit.Chem import Descriptors, Crippen, Lipinski, rdMolDescriptors, QED
def canonicalize(smiles: str):
mol = Chem.MolFromSmiles(str(smiles))
if mol is None:
return None, None
can = Chem.MolToSmiles(mol, isomericSmiles=True)
return mol, can
def try_sa_score(mol):
"""
Compute RDKit SA score.
RDKit Contrib is installed in different locations depending on the package source.
Common conda location:
$CONDA_PREFIX/share/RDKit/Contrib/SA_Score/sascorer.py
Lower score means easier synthesis.
Higher score means harder synthesis.
"""
import_paths = []
# First try the normal Python import.
try:
from rdkit.Contrib.SA_Score import sascorer
return float(sascorer.calculateScore(mol))
except Exception:
pass
# Then search common RDKit Contrib locations.
conda_prefix = os.environ.get("CONDA_PREFIX")
if conda_prefix:
import_paths.append(Path(conda_prefix) / "share" / "RDKit" / "Contrib" / "SA_Score")
import_paths.extend([
Path("/opt/conda/envs/ailixir/share/RDKit/Contrib/SA_Score"),
Path("/opt/conda/share/RDKit/Contrib/SA_Score"),
Path("/usr/share/RDKit/Contrib/SA_Score"),
Path("/usr/local/share/RDKit/Contrib/SA_Score"),
])
for p in import_paths:
sascorer_file = p / "sascorer.py"
fpscores_file = p / "fpscores.pkl.gz"
if sascorer_file.exists() and fpscores_file.exists():
sys.path.insert(0, str(p))
try:
import sascorer
return float(sascorer.calculateScore(mol))
except Exception:
continue
return None
def compute_properties(smiles: str):
mol, can = canonicalize(smiles)
if mol is None:
return {
"valid": False,
"canonical_smiles": None,
"mw": None,
"logp": None,
"tpsa": None,
"hbd": None,
"hba": None,
"rot_bonds": None,
"qed": None,
"sa_score": None,
}
return {
"valid": True,
"canonical_smiles": can,
"mw": float(Descriptors.MolWt(mol)),
"logp": float(Crippen.MolLogP(mol)),
"tpsa": float(rdMolDescriptors.CalcTPSA(mol)),
"hbd": int(Lipinski.NumHDonors(mol)),
"hba": int(Lipinski.NumHAcceptors(mol)),
"rot_bonds": int(Lipinski.NumRotatableBonds(mol)),
"qed": float(QED.qed(mol)),
"sa_score": try_sa_score(mol),
}
def call_affinity_api(smiles_list, url: str):
if not smiles_list:
return []
try:
response = requests.post(
url,
json={"smiles": smiles_list},
timeout=300,
)
response.raise_for_status()
payload = response.json()
preds = payload.get("pred_pAff_mean")
if preds is None:
raise RuntimeError(
f"Affinity API response missing 'pred_pAff_mean'. Response was: {json.dumps(payload)[:500]}"
)
return [float(x) for x in preds]
except Exception as e:
print(f"Warning: Affinity API call failed ({e}). Returning null predictions.")
return [None] * len(smiles_list)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, help="Input generated_smiles.csv from REINVENT sampling.")
parser.add_argument("--output", required=True, help="Output enriched CSV.")
parser.add_argument(
"--affinity-url",
default="http://127.0.0.1:8001/reinvent_predict",
help="DeepPurpose/FastAPI affinity endpoint.",
)
parser.add_argument(
"--top-k",
type=int,
default=0,
help="If >0, keep only top K rows after sorting by pred_pAff_mean desc then QED desc.",
)
args = parser.parse_args()
input_path = Path(args.input)
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
df = pd.read_csv(input_path)
if "SMILES" not in df.columns:
raise ValueError(f"Input file must contain a SMILES column. Found columns: {list(df.columns)}")
props = [compute_properties(smi) for smi in df["SMILES"].tolist()]
props_df = pd.DataFrame(props)
out = pd.concat([df.reset_index(drop=True), props_df.reset_index(drop=True)], axis=1)
valid_mask = out["valid"] == True
valid_smiles = out.loc[valid_mask, "canonical_smiles"].tolist()
pred_values = call_affinity_api(valid_smiles, args.affinity_url)
out["pred_pAff_mean"] = None
out.loc[valid_mask, "pred_pAff_mean"] = pred_values
# Docking is optional in v1. Keep stable columns for frontend/backend filters.
out["docking_score"] = None
out["docking_status"] = "not_run"
out["docking_pose_file"] = None
# Sort for display only.
# This is not a final scientific ranking.
out = out.sort_values(
by=["pred_pAff_mean", "qed"],
ascending=[False, False],
)
if args.top_k and args.top_k > 0:
out = out.head(args.top_k)
out.to_csv(output_path, index=False)
print(f"Wrote: {output_path}")
print(f"Rows: {len(out)}")
print("Columns:", ", ".join(out.columns))
if __name__ == "__main__":
main()
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