banner("[2/9] BIOACTIVITY MINING (ChEMBL activities -> pIC50)")
def pull_activities(tid, cap):
url, rows = f"{BASE}/activity", []
params = {"target_chembl_id": tid, "standard_type": "IC50",
"pchembl_value__isnull": "false", "limit": 1000, "format": "json"}
js = http_json(url, params)
pages = 0
while js and pages < 60:
rows.extend(js.get("activities", []))
pages += 1
if len(rows) >= cap:
break
nxt = js.get("page_meta", {}).get("next")
if not nxt:
break
nurl = nxt if nxt.startswith("http") else " + nxt
js = http_json(nurl)
return rows[:cap]
raw = pull_activities(target_id, MAX_ACTIVITIES)
print(f" Pulled {len(raw)} raw IC50 records with a curated pChEMBL value.")
recs = []
for a in raw:
smi, pv = a.get("canonical_smiles"), a.get("pchembl_value")
if not smi or pv in (None, ""):
continue
if a.get("standard_relation") != "=":
continue
if a.get("standard_units") not in ("nM", None):
continue
try:
pv = float(pv)
except Exception:
continue
recs.append({"chembl_id": a.get("molecule_chembl_id"), "smiles": smi, "pIC50": pv})
raw_df = pd.DataFrame(recs, columns=["chembl_id", "smiles", "pIC50"])
print(f" After quality filters: {len(raw_df)} measurements.")
if len(raw_df) == 0:
print("n STOP: no usable IC50 data was retrieved for this target.n"
" Fix: set TARGET_CHEMBL_ID to a target that has inhibitor datan"
" (e.g. CHEMBL203=EGFR, CHEMBL5251=BTK, CHEMBL2971=JAK2),n"
" or set TARGET_CHEMBL_ID="" to auto-resolve TARGET_QUERY by data volume.")
raise SystemExit("No bioactivity data for the selected target.")
banner("[3/9] MOLECULAR CURATION (standardize, de-salt, aggregate)")
_lfc = rdMolStandardize.LargestFragmentChooser() if _HAS_STD else None
_unc = rdMolStandardize.Uncharger() if _HAS_STD else None
def standardize(smi):
m = Chem.MolFromSmiles(smi)
if m is None:
return None, None
try:
if _HAS_STD:
m = _lfc.choose(m); m = _unc.uncharge(m)
else:
frags = Chem.GetMolFrags(m, asMols=True, sanitizeFrags=True)
if frags:
m = max(frags, key=lambda x: x.GetNumHeavyAtoms())
return m, Chem.MolToSmiles(m)
except Exception:
return None, None
canon, keep_mol = [], {}
for _, r in raw_df.iterrows():
m, cs = standardize(r["smiles"])
if cs is None or m.GetNumHeavyAtoms() < 6:
continue
canon.append({"smiles": cs, "pIC50": r["pIC50"], "chembl_id": r["chembl_id"]})
keep_mol[cs] = m
cdf = pd.DataFrame(canon, columns=["smiles", "pIC50", "chembl_id"])
data = (cdf.groupby("smiles")
.agg(pIC50=("pIC50", "median"), n=("pIC50", "size"),
chembl_id=("chembl_id", "first")).reset_index())
if len(data) > MAX_UNIQUE:
data = data.sample(MAX_UNIQUE, random_state=RANDOM_STATE).reset_index(drop=True)
data["mol"] = data["smiles"].map(keep_mol)
n_active = int((data["pIC50"] >= ACTIVE_PIC50).sum())
print(f" Unique curated molecules : {len(data)}")
print(f" Potent actives (IC50<=100nM): {n_active} ({100*n_active/len(data):.1f}%)")
print(f" pIC50 range: {data.pIC50.min():.2f} - {data.pIC50.max():.2f} "
f"(median {data.pIC50.median():.2f})")
mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=RADIUS, fpSize=NBITS)
DESC = [("MolWt", Descriptors.MolWt), ("MolLogP", Descriptors.MolLogP),
("TPSA", Descriptors.TPSA), ("HBD", Descriptors.NumHDonors),
("HBA", Descriptors.NumHAcceptors), ("RotB", Descriptors.NumRotatableBonds),
("AromRings", Descriptors.NumAromaticRings), ("FracCSP3", Descriptors.FractionCSP3),
("HeavyAtoms", Descriptors.HeavyAtomCount),
("NumRings", lambda m: rdMolDescriptors.CalcNumRings(m))]
FEAT_NAMES = [f"bit_{i}" for i in range(NBITS)] + [n for n, _ in DESC]
def fp_array(m):
a = np.zeros((NBITS,), dtype=np.int8)
DataStructs.ConvertToNumpyArray(mfpgen.GetFingerprint(m), a)
return a
def featurize(mols):
Xb = np.zeros((len(mols), NBITS), dtype=np.int8)
Xd = np.zeros((len(mols), len(DESC)), dtype=np.float32)
for i, m in enumerate(mols):
Xb[i] = fp_array(m)
for j, (_, fn) in enumerate(DESC):
try:
Xd[i, j] = fn(m)
except Exception:
Xd[i, j] = 0.0
return np.nan_to_num(np.hstack([Xb, Xd]).astype(np.float32))
X = featurize(list(data["mol"]))
y = data["pIC50"].values
print(f" Feature matrix: {X.shape[0]} molecules x {X.shape[1]} features "
f"({NBITS} ECFP bits + {len(DESC)} descriptors)")
