|
|
| import pandas as pd, numpy as np, re |
| from pathlib import Path |
| from transformers import AutoTokenizer, AutoModel |
| import torch |
|
|
| DATA_PROC = Path("data/processed") |
|
|
| def gen_alcohols(n=150): |
| base=[] |
| for c in range(1,21): |
| base.append(("ALK_%02d"%c, "C"*c + "O")) |
| for c in range(3,13): |
| base.append((f"IALK_{c}", "C(C)" + "C"*(c-2) + "O")) |
| return base[:n] |
|
|
| def gen_aromatics(n=200): |
| subs = ["Cl","Br","F","N(=O)=O","C(=O)O","C#N","OCC","CCN","CC(=O)O"] |
| out=[]; k=0 |
| for s in subs: |
| for rpos in ["c1ccccc1", "c1ccc(cc1)"]: |
| out.append((f"ARO_{k:03d}", rpos.replace("c","c") + s)); k+=1 |
| if k>=n: return out |
| return out |
|
|
| def gen_heterocycles(n=200): |
| rings = ["c1ncccc1", "c1occcn1", "n1ccccc1", "c1ccncc1", "c1ccsc1", "c1ncncn1"] |
| out=[]; k=0 |
| for r in rings: |
| out.append((f"HET_{k:03d}", r)); k+=1 |
| out.append((f"HETOH_{k:03d}", r+"O")); k+=1 |
| if k>=n: break |
| while len(out)<n: |
| out.append((f"HETPAD_{len(out):03d}", "c1ncncn1")) |
| return out[:n] |
|
|
| controls = [("ETHANOL","CCO"), ("H2O2","OO")] |
| lib = controls + gen_alcohols(180) + gen_aromatics(220) + gen_heterocycles(210) |
| L0 = pd.DataFrame(lib, columns=["compound","smiles"]).drop_duplicates("compound").reset_index(drop=True) |
|
|
| L0["chembl_id"] = pd.NA |
| L0["pubchem_cid"] = pd.NA |
| def infer_class(s): |
| s=str(s) |
| if s=="CCO": return "solvent" |
| if s=="OO": return "oxidant" |
| if "c1" in s: return "aromatic/heterocycle" |
| if s.endswith("O"): return "alcohol" |
| return "other" |
| L0["class"] = L0["smiles"].map(infer_class) |
| L0["is_control"] = L0["compound"].isin(["ETHANOL","H2O2"]) |
|
|
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
| tok_c = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-77M-MLM", padding=True, truncation=True) |
| mdl_c = AutoModel.from_pretrained("DeepChem/ChemBERTa-77M-MLM", use_safetensors=True).eval().to(DEVICE) |
|
|
| @torch.no_grad() |
| def chemberta_embed(smi: str): |
| smi = "".join(str(smi).split()) |
| toks = tok_c(smi, return_tensors="pt", truncation=True, max_length=128) |
| toks = {k:v.to(DEVICE) for k,v in toks.items()} |
| hs = mdl_c(**toks).last_hidden_state |
| return hs[:,0,:].squeeze(0).cpu().numpy() |
|
|
| rows=[] |
| for r in L0.itertuples(index=False): |
| v = chemberta_embed(r.smiles) |
| rows.append([r.compound] + v.tolist()) |
| L = pd.DataFrame(rows, columns=["compound"]+[f"d{i}" for i in range(len(rows[0])-1)]) |
| L = L.merge(L0, on="compound", how="left") |
|
|
| num = L.select_dtypes(include=[float,int]).columns |
| L[num] = L[num].fillna(L[num].median(numeric_only=True)) |
| L.to_csv(DATA_PROC/"ligand.csv", index=False) |
| print("ligand.csv ->", DATA_PROC/"ligand.csv", "| shape:", L.shape) |
