diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..bd1ad7af9ca0cec1a0b5f61bc1db7de94f2c3529 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,113 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +ames/seed1/catboost.cbm filter=lfs diff=lfs merge=lfs -text +ames/seed2/catboost.cbm filter=lfs diff=lfs merge=lfs -text +ames/seed3/catboost.cbm filter=lfs diff=lfs merge=lfs -text +ames/seed4/catboost.cbm filter=lfs diff=lfs merge=lfs -text +ames/seed5/catboost.cbm filter=lfs diff=lfs merge=lfs -text +bbb_martins/seed1/catboost.cbm filter=lfs diff=lfs merge=lfs -text +bbb_martins/seed2/catboost.cbm filter=lfs diff=lfs merge=lfs -text +bbb_martins/seed3/catboost.cbm filter=lfs diff=lfs merge=lfs -text +bbb_martins/seed4/catboost.cbm filter=lfs diff=lfs merge=lfs -text +bbb_martins/seed5/catboost.cbm 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diff=lfs merge=lfs -text +vdss_lombardo/seed1/catboost.cbm filter=lfs diff=lfs merge=lfs -text +vdss_lombardo/seed2/catboost.cbm filter=lfs diff=lfs merge=lfs -text +vdss_lombardo/seed3/catboost.cbm filter=lfs diff=lfs merge=lfs -text +vdss_lombardo/seed4/catboost.cbm filter=lfs diff=lfs merge=lfs -text +vdss_lombardo/seed5/catboost.cbm filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..1abad932bdec5f9b19eb190fc07e5b7ae9381b5b --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2026 ADMET endpoint models contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..75c123261f6ee16a54d34be10159e2df9a19168d --- /dev/null +++ b/README.md @@ -0,0 +1,167 @@ +--- +license: apache-2.0 +library_name: catboost +tags: + - chemistry + - drug-discovery + - admet + - catboost +--- + +# Endpoint-specific CatBoost models for the TDC ADMET benchmark + +22 个 TDC ADMET 终点各自一份 CatBoost 模型；每个终点 5 个官方种子，预测取 5 个种子的平均值。 + +This repository contains the released fitted parameters of the endpoint-specific CatBoost model family for the [Therapeutics Data Commons](https://tdcommons.ai/) (TDC) ADMET benchmark. Feature blocks and hyperparameters were selected per endpoint under the official benchmark protocol. + +## Overview + +- **22 endpoints**, one directory each, named after the TDC dataset (lower-cased). +- **5 official seeds** per endpoint (`seed1` to `seed5`). Each `catboost.cbm` is fitted on that seed's official training partition. +- **Prediction** is the mean over the five seeds. + - Classification: the score is the positive-class probability. + - Regression: the score is on the original label scale (the target scaler is inverted using `y_scaler.json`). +- **Optional feature blocks** (PaDEL 3D descriptors, GIN embeddings, MiniMol embeddings) are used only by the endpoints that kept them; see the [endpoint table](#endpoints). +- **`match.json`** is an audit log from the refit only: every saved test prediction matched the reported vector with a maximum absolute difference of 0 at `atol = 1e-5`. It is not needed to load or run a model. + +## Quick start + +Install the dependencies from `requirements.txt`: + +| Package | Version | +| --- | --- | +| Python | 3.11.16 | +| `catboost` | 1.2.10 | +| `numpy` | 1.26.4 | +| `rdkit` | 2026.03.6 | + +Endpoints that use only the fingerprint block need nothing else: + +```python +from predict import EndpointModel + +model = EndpointModel.from_pretrained(".", "pgp_broccatelli") +print(model.predict(["CCO", "c1ccccc1"])) +``` + +Endpoints that kept extra blocks need those arrays supplied by the caller: + +```python +from predict import EndpointModel + +model = EndpointModel.from_pretrained(".", "herg") + +# padel: (n, 431) in feature_spec["padel_3d"]["columns"] order, +# or (n, n_3d) already sliced to the endpoint's kept columns +# gin: (n, 300) from the frozen GIN encoder described below +# minimol: (n, 512) from MiniMol v1; required when blend_weight > 0 +# or minimol_concat is true +pred = model.predict(smiles, padel=padel, gin=gin, minimol=minimol) +``` + +The MiniMol blend head is stored as `minimol_head.npz`, which is the preferred format and does not require scikit-learn. `minimol_head.joblib` is kept as the original fitted object. + +## Endpoints + +The directory name is the second argument to `EndpointModel.from_pretrained`. The metric is the official TDC ADMET metric for that endpoint. Columns **3D**, **GIN**, **MiniMol concat**, and **Blend** show which optional feature blocks the endpoint uses (`3D` is the number of PaDEL 3D columns kept; `Blend` is `blend_weight`). + +| Directory | TDC name | Task | Metric | 3D | GIN | MiniMol concat | Blend | +| --- | --- | --- | --- | --- | --- | --- | --- | +| `dili` | DILI | classification | ROC-AUC | 431 | | | 0.1 | +| `hia_hou` | HIA_Hou | classification | ROC-AUC | 0 | yes | | 0 | +| `bioavailability_ma` | Bioavailability_Ma | classification | ROC-AUC | 431 | yes | yes | 0.1 | +| `herg` | hERG | classification | ROC-AUC | 200 | yes | | 0.2 | +| `half_life_obach` | Half_Life_Obach | regression | Spearman | 431 | | | 0.8 | +| `cyp2d6_substrate_carbonmangels` | CYP2D6_Substrate_CarbonMangels | classification | PR-AUC | 100 | yes | | 0 | +| `cyp2c9_substrate_carbonmangels` | CYP2C9_Substrate_CarbonMangels | classification | PR-AUC | 100 | | | 0 | +| `cyp3a4_substrate_carbonmangels` | CYP3A4_Substrate_CarbonMangels | classification | ROC-AUC | 431 | | | 0.2 | +| `caco2_wang` | Caco2_Wang | regression | MAE | 431 | | | 0 | +| `clearance_microsome_az` | Clearance_Microsome_AZ | regression | Spearman | 431 | yes | yes | 0 | +| `vdss_lombardo` | VDss_Lombardo | regression | Spearman | 431 | | yes | 0 | +| `clearance_hepatocyte_az` | Clearance_Hepatocyte_AZ | regression | Spearman | 431 | | | 0 | +| `pgp_broccatelli` | Pgp_Broccatelli | classification | ROC-AUC | 0 | | | 0 | +| `bbb_martins` | BBB_Martins | classification | ROC-AUC | 431 | | | 0 | +| `ppbr_az` | PPBR_AZ | regression | MAE | 25 | yes | | 0.1 | +| `lipophilicity_astrazeneca` | Lipophilicity_AstraZeneca | regression | MAE | 431 | | | 0.3 | +| `ames` | AMES | classification | ROC-AUC | 0 | | yes | 0.2 | +| `ld50_zhu` | LD50_Zhu | regression | MAE | 0 | | | 0.2 | +| `solubility_aqsoldb` | Solubility_AqSolDB | regression | MAE | 50 | | | 0.2 | +| `cyp2c9_veith` | CYP2C9_Veith | classification | PR-AUC | 0 | yes | | 0.2 | +| `cyp3a4_veith` | CYP3A4_Veith | classification | PR-AUC | 200 | yes | | 0.1 | +| `cyp2d6_veith` | CYP2D6_Veith | classification | PR-AUC | 431 | yes | yes | 0 | + +## Features + +### Fingerprint block (all endpoints) + +Every model starts with the same **2563** fingerprint columns, in this order: + +1. Morgan count, radius 2, 1024 bits (`GetHashedMorganFingerprint`) +2. Avalon count, 1024 bits (`GetAvalonCountFP`) +3. ErG, 315 +4. 200 RDKit descriptors, named in `feature_spec.json` + +`predict` computes this block directly from SMILES. A SMILES string that RDKit cannot parse becomes a NaN row, which CatBoost accepts. + +### Optional blocks appended after the fingerprint + +| Block | Used when | Width | Supplied via | +| --- | --- | --- | --- | +| PaDEL 3D | `n_3d > 0` | `n_3d` | `padel` | +| GIN | `gin` is true | 300 | `gin` | +| MiniMol | `minimol_concat` is true | 512 | `minimol` | + +**PaDEL 3D (431 columns).** +- Only the 3D descriptor group is used (PaDEL flags `-3d -retain3d -retainorder`). +- Conformers come from RDKit ETKDGv3 with `randomSeed=1`, hydrogens kept, then MMFF optimization with `maxIters=200`. +- If the first embedding fails, it is retried with `useRandomCoords=True`. A molecule that still fails is omitted, and its 3D descriptors stay missing. +- Some endpoints keep a truncated, ranked subset of the 431 columns. The kept names and their indices into the 431-column list are stored in `feature_spec.json` under `endpoints..padel_columns` and `padel_index`. An endpoint with `n_3d` = 431 uses the full list in that order. + +**GIN (300 dimensions).** +- Encoder: `dgllife.model.load_pretrained("gin_supervised_masking")`. +- Graphs: `mol_to_bigraph(add_self_loop=True, canonical_atom_order=False)` with `PretrainAtomFeaturizer` and `PretrainBondFeaturizer`. +- Readout: `dgl` `AvgPooling`. +- **The encoder weights are not bundled** in this repository. + +**MiniMol (512 dimensions).** +- Embeddings come from public MiniMol v1. +- **The encoder weights are not bundled** in this repository. + +### MiniMol blend head + +Where `blend_weight` is above 0, each seed also has a small head on the MiniMol vector: `StandardScaler`, one hidden layer of 128 ReLU units, then a linear output. The per-seed prediction is + +``` +(1 - alpha) * CatBoost + alpha * head +``` + +where `alpha` is the endpoint's `blend_weight`. If a row's MiniMol vector is not finite, that row keeps the CatBoost prediction unchanged. + +### Known limitations + +- **Unfeaturizable SMILES.** Two solubility-test molecules have no MiniMol vector and fall back to CatBoost: `CC1=CC=C[NH+2]([O-])[CH-]1` and `O=C(O)C1=C[NH+2]([O-])[CH-]C=C1`. +- **CYP2D6 substrate class weighting.** `CYP2D6_Substrate_CarbonMangels` was fitted with `scale_pos_weight` = 3. This weight is already baked into its trees; do not apply it again. +- **External encoders.** GIN and MiniMol embeddings must be computed by the caller with the specifications above. Mismatched encoder versions or featurization settings will change predictions. + +## Repository layout + +``` +/config.json +/match.json # audit log only +/seed{1-5}/catboost.cbm +/seed{1-5}/y_scaler.json # regression only +/seed{1-5}/minimol_head.npz # blend_weight > 0 (preferred) +/seed{1-5}/minimol_head.joblib # same head, original object +feature_spec.json +versions.json +predict.py +requirements.txt +``` + +The largest checkpoint is under 10 MiB, so this repository does not need Git LFS. + +## License + +Released under the Apache License 2.0. Copyright 2026 ADMET endpoint models contributors. + +Third-party fingerprints, PaDEL, GIN, and MiniMol remain under their own licenses. 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"morgan": { + "kind": "count", + "radius": 2, + "n_bits": 1024, + "function": "GetHashedMorganFingerprint" + }, + "avalon": { + "kind": "count", + "n_bits": 1024, + "function": "GetAvalonCountFP" + }, + "erg_dim": 315, + "descriptor_dim": 200, + "dim": 2563 + }, + "rdkit_descriptors": [ + "BalabanJ", + "BertzCT", + "Chi0", + "Chi0n", + "Chi0v", + "Chi1", + "Chi1n", + "Chi1v", + "Chi2n", + "Chi2v", + "Chi3n", + "Chi3v", + "Chi4n", + "Chi4v", + "EState_VSA1", + "EState_VSA10", + "EState_VSA11", + "EState_VSA2", + "EState_VSA3", + "EState_VSA4", + "EState_VSA5", + "EState_VSA6", + "EState_VSA7", + "EState_VSA8", + "EState_VSA9", + "ExactMolWt", + "FpDensityMorgan1", + "FpDensityMorgan2", + "FpDensityMorgan3", + "FractionCSP3", + "HallKierAlpha", + "HeavyAtomCount", + "HeavyAtomMolWt", + "Ipc", + "Kappa1", + "Kappa2", + "Kappa3", + "LabuteASA", + "MaxAbsEStateIndex", + "MaxAbsPartialCharge", + "MaxEStateIndex", + "MaxPartialCharge", + "MinAbsEStateIndex", + "MinAbsPartialCharge", + "MinEStateIndex", + "MinPartialCharge", + "MolLogP", + "MolMR", + "MolWt", + "NHOHCount", + "NOCount", + "NumAliphaticCarbocycles", + "NumAliphaticHeterocycles", + "NumAliphaticRings", + "NumAromaticCarbocycles", + "NumAromaticHeterocycles", + "NumAromaticRings", + "NumHAcceptors", + "NumHDonors", + "NumHeteroatoms", + "NumRadicalElectrons", + "NumRotatableBonds", + "NumSaturatedCarbocycles", + "NumSaturatedHeterocycles", + "NumSaturatedRings", + "NumValenceElectrons", + "PEOE_VSA1", + "PEOE_VSA10", + "PEOE_VSA11", + "PEOE_VSA12", + "PEOE_VSA13", + "PEOE_VSA14", + "PEOE_VSA2", + "PEOE_VSA3", + "PEOE_VSA4", + "PEOE_VSA5", + "PEOE_VSA6", + "PEOE_VSA7", + "PEOE_VSA8", + "PEOE_VSA9", + "RingCount", + "SMR_VSA1", + "SMR_VSA10", + "SMR_VSA2", + "SMR_VSA3", + "SMR_VSA4", + "SMR_VSA5", + "SMR_VSA6", + "SMR_VSA7", + "SMR_VSA8", + "SMR_VSA9", + "SlogP_VSA1", + "SlogP_VSA10", + "SlogP_VSA11", + "SlogP_VSA12", + "SlogP_VSA2", + "SlogP_VSA3", + "SlogP_VSA4", + "SlogP_VSA5", + "SlogP_VSA6", + "SlogP_VSA7", + "SlogP_VSA8", + "SlogP_VSA9", + "TPSA", + "VSA_EState1", + "VSA_EState10", + "VSA_EState2", + "VSA_EState3", + "VSA_EState4", + "VSA_EState5", + "VSA_EState6", + "VSA_EState7", + "VSA_EState8", + "VSA_EState9", + "fr_Al_COO", + "fr_Al_OH", + "fr_Al_OH_noTert", + "fr_ArN", + "fr_Ar_COO", + "fr_Ar_N", + "fr_Ar_NH", + "fr_Ar_OH", + "fr_COO", + "fr_COO2", + "fr_C_O", + "fr_C_O_noCOO", + "fr_C_S", + "fr_HOCCN", + "fr_Imine", + "fr_NH0", + "fr_NH1", + "fr_NH2", + "fr_N_O", + "fr_Ndealkylation1", + "fr_Ndealkylation2", + "fr_Nhpyrrole", + "fr_SH", + "fr_aldehyde", + "fr_alkyl_carbamate", + "fr_alkyl_halide", + "fr_allylic_oxid", + "fr_amide", + "fr_amidine", + "fr_aniline", + "fr_aryl_methyl", + "fr_azide", + "fr_azo", + "fr_barbitur", + "fr_benzene", + "fr_benzodiazepine", + "fr_bicyclic", + "fr_diazo", + "fr_dihydropyridine", + "fr_epoxide", + "fr_ester", + "fr_ether", + "fr_furan", + "fr_guanido", + "fr_halogen", + "fr_hdrzine", + "fr_hdrzone", + "fr_imidazole", + "fr_imide", + "fr_isocyan", + "fr_isothiocyan", + "fr_ketone", + "fr_ketone_Topliss", + "fr_lactam", + "fr_lactone", + "fr_methoxy", + "fr_morpholine", + "fr_nitrile", + "fr_nitro", + "fr_nitro_arom", + "fr_nitro_arom_nonortho", + "fr_nitroso", + "fr_oxazole", + "fr_oxime", + "fr_para_hydroxylation", + "fr_phenol", + "fr_phenol_noOrthoHbond", + "fr_phos_acid", + "fr_phos_ester", + "fr_piperdine", + "fr_piperzine", + "fr_priamide", + "fr_prisulfonamd", + "fr_pyridine", + "fr_quatN", + "fr_sulfide", + "fr_sulfonamd", + "fr_sulfone", + "fr_term_acetylene", + "fr_tetrazole", + "fr_thiazole", + "fr_thiocyan", + "fr_thiophene", + "fr_unbrch_alkane", + "fr_urea", + "qed" + ], + "padel_3d": { + "n_columns": 431, + "columns": [ + "TDB1u", + "TDB2u", + "TDB3u", + "TDB4u", + "TDB5u", + "TDB6u", + "TDB7u", + "TDB8u", + "TDB9u", + "TDB10u", + "TDB1m", + "TDB2m", + "TDB3m", + "TDB4m", + "TDB5m", + "TDB6m", + "TDB7m", + "TDB8m", + "TDB9m", + "TDB10m", + "TDB1v", + "TDB2v", + "TDB3v", + "TDB4v", + 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Fingerprints are computed here. +PaDEL 3D columns, the GIN embedding, and the MiniMol embedding are inputs: +their encoder weights are not part of this release. + + from predict import EndpointModel + model = EndpointModel.from_pretrained(".", "pgp_broccatelli") + model.predict(["CCO", "c1ccccc1"]) +""" +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np + + +def _read_json(path: Path) -> dict: + return json.loads(path.read_text()) + + +def invert_y(raw: np.ndarray, state: dict) -> np.ndarray: + y = np.asarray(raw, dtype=float).reshape(-1) * float(state["scale"]) + float(state["mean"]) + if state["log"]: + y = np.power(10.0, y) - 1.0 + return y + float(state["offset"]) + + +def _mlp(x: np.ndarray, head: dict) -> np.ndarray: + """One hidden ReLU layer, matching the exported scikit-learn head.""" + hidden = np.maximum(0.0, x @ head["coef_0"] + head["intercept_0"]) + out = hidden @ head["coef_1"] + head["intercept_1"] + out = np.asarray(out, dtype=float) + if out.ndim == 1 or out.shape[-1] == 1: + return out.reshape(-1) + if head["kind"] != "classifier": + raise RuntimeError("regressor head has more than one output") + shifted = out - out.max(axis=1, keepdims=True) + exp = np.exp(shifted) + return (exp / exp.sum(axis=1, keepdims=True))[:, 1] + + +def head_predict(minimol: np.ndarray, head: dict) -> np.ndarray: + pred = np.full(len(minimol), np.nan, dtype=float) + ok = np.isfinite(minimol).all(axis=1) + if not ok.any(): + return pred + scaled = (minimol[ok] - head["x_mean"]) / head["x_scale"] + raw = _mlp(scaled, head) + if head["kind"] == "classifier": + if str(head["activation"]) == "logistic" and head["coef_1"].shape[-1] == 1: + raw = 1.0 / (1.0 + np.exp(-raw)) + pred[ok] = raw + return pred + pred[ok] = invert_y(raw, head) + return pred + + +def mix(catboost: np.ndarray, head: np.ndarray, weight: float) -> np.ndarray: + blended = (1.0 - weight) * catboost + weight * head + missing = ~np.isfinite(head) + blended[missing] = catboost[missing] + return blended + + +def fingerprints(smiles: list[str], descriptor_names: list[str]) -> np.ndarray: + """Morgan count r=2 1024, Avalon count 1024, ErG 315, then the named RDKit descriptors.""" + from rdkit import Chem, RDLogger + from rdkit.Avalon.pyAvalonTools import GetAvalonCountFP + from rdkit.Chem import DataStructs, rdReducedGraphs + from rdkit.Chem.rdMolDescriptors import GetHashedMorganFingerprint + from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator + + RDLogger.DisableLog("rdApp.*") + calculator = MolecularDescriptorCalculator(descriptor_names) + rows = [] + for one in smiles: + mol = Chem.MolFromSmiles(one) if isinstance(one, str) else None + if mol is None: + rows.append(None) + continue + morgan = np.zeros((0,), dtype=np.int32) + DataStructs.ConvertToNumpyArray( + GetHashedMorganFingerprint(mol, nBits=1024, radius=2), morgan + ) + avalon = np.zeros((0,), dtype=np.int32) + DataStructs.ConvertToNumpyArray(GetAvalonCountFP(mol, nBits=1024), avalon) + erg = np.asarray(rdReducedGraphs.GetErGFingerprint(mol), dtype=float) + desc = np.asarray(calculator.CalcDescriptors(mol), dtype=float) + rows.append(np.concatenate([morgan, avalon, erg, desc]).astype(float)) + width = next(len(row) for row in rows if row is not None) + out = np.full((len(smiles), width), np.nan, dtype=float) + for i, row in enumerate(rows): + if row is not None: + out[i] = row + return out + + +def _as_2d(block, width: int, name: str, n_rows: int) -> np.ndarray: + array = np.asarray(block, dtype=float) + if array.ndim != 2 or array.shape != (n_rows, width): + raise ValueError(f"{name} must have shape ({n_rows}, {width}), got {array.shape}") + return array + + +class EndpointModel: + def __init__(self, root: Path, endpoint: str): + self.root = Path(root) + self.endpoint = endpoint + self.config = _read_json(self.root / endpoint / "config.json") + self.spec = _read_json(self.root / "feature_spec.json") + self.endpoint_spec = self.spec["endpoints"][endpoint] + if self.config["endpoint"] != endpoint: + raise RuntimeError(f"{endpoint} config says {self.config['endpoint']}") + + @classmethod + def from_pretrained(cls, root: str | Path, endpoint: str) -> "EndpointModel": + return cls(Path(root), endpoint) + + def _padel_block(self, padel, n_rows: int) -> np.ndarray: + n_keep = int(self.config["n_3d"]) + if n_keep == 0: + if padel is not None: + raise ValueError(f"{self.endpoint} does not use PaDEL columns") + return np.zeros((n_rows, 0), dtype=float) + if padel is None: + raise ValueError( + f"{self.endpoint} expects PaDEL 3D columns. " + "Pass an array of shape (n, 431) in feature_spec padel_3d column order, " + "or an array already reduced to this endpoint's columns." + ) + if hasattr(padel, "columns"): + names = list(self.endpoint_spec["padel_columns"]) + missing = [name for name in names if name not in padel.columns] + if missing: + raise ValueError(f"PaDEL frame is missing {missing[:5]}") + return np.asarray(padel[names], dtype=float) + array = np.asarray(padel, dtype=float) + if array.ndim != 2 or array.shape[0] != n_rows: + raise ValueError(f"padel must have {n_rows} rows, got {getattr(array, 'shape', None)}") + canonical = len(self.spec["padel_3d"]["columns"]) + if array.shape[1] == canonical: + index = self.endpoint_spec["padel_index"] + return np.ascontiguousarray(array[:, index]) + if array.shape[1] == n_keep: + return array + raise ValueError( + f"padel second dimension must be {canonical} or {n_keep}, got {array.shape[1]}" + ) + + def features(self, smiles: list[str], padel=None, gin=None, minimol=None) -> np.ndarray: + fp = fingerprints(smiles, self.spec["rdkit_descriptors"]) + if fp.shape[1] != int(self.config["n_fingerprint"]): + raise RuntimeError(f"fingerprint width {fp.shape[1]} != {self.config['n_fingerprint']}") + blocks = [fp, self._padel_block(padel, len(smiles))] + if self.config["gin"]: + if gin is None: + raise ValueError(f"{self.endpoint} expects a GIN embedding of shape (n, 300)") + blocks.append(_as_2d(gin, 300, "gin", len(smiles))) + if self.config["minimol_concat"]: + if minimol is None: + raise ValueError(f"{self.endpoint} expects a MiniMol embedding of shape (n, 512)") + blocks.append(_as_2d(minimol, 512, "minimol", len(smiles))) + x = np.ascontiguousarray(np.hstack(blocks)) + if x.shape[1] != int(self.config["n_features"]): + raise RuntimeError(f"feature width {x.shape[1]} != {self.config['n_features']}") + return x + + def predict_features(self, x: np.ndarray, minimol=None) -> np.ndarray: + from catboost import CatBoostClassifier, CatBoostRegressor + + x = np.asarray(x, dtype=float) + if x.ndim != 2 or x.shape[1] != int(self.config["n_features"]): + raise ValueError(f"x must have shape (n, {self.config['n_features']})") + weight = float(self.config["blend_weight"]) + if weight > 0.0: + minimol = _as_2d(minimol, 512, "minimol", len(x)) + task = self.config["task"] + klass = CatBoostRegressor if task == "regression" else CatBoostClassifier + seeds = [] + for seed in self.config["seeds"]: + folder = self.root / self.endpoint / f"seed{seed}" + model = klass() + model.load_model(str(folder / "catboost.cbm")) + if task == "regression": + pred = invert_y(model.predict(x), _read_json(folder / "y_scaler.json")) + else: + pred = np.asarray(model.predict_proba(x)[:, 1], dtype=float) + if weight > 0.0: + head = dict(np.load(folder / "minimol_head.npz")) + pred = mix(pred, head_predict(minimol, head), weight) + seeds.append(pred) + return np.mean(seeds, axis=0) + + def predict(self, smiles, padel=None, gin=None, minimol=None) -> np.ndarray: + if isinstance(smiles, str): + smiles = [smiles] + smiles = list(smiles) + x = self.features(smiles, padel=padel, gin=gin, minimol=minimol) + return self.predict_features(x, minimol=minimol) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fecc7ccc776501bf6f2fdc484babec8342fbcc48 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +catboost==1.2.10 +numpy==1.26.4 +rdkit==2026.03.6 diff --git a/solubility_aqsoldb/config.json b/solubility_aqsoldb/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f1c6edec6f807e884fa1646f6547e0317147b7fc --- /dev/null +++ b/solubility_aqsoldb/config.json @@ -0,0 +1,30 @@ +{ + "endpoint": "solubility_aqsoldb", + "task": "regression", + "log_scale": false, + "tree": { + "depth": 6, + "iterations": 1000, + "learning_rate": 0.1, + "l2_leaf_reg": 10.0 + }, + "blend_weight": 0.2, + "scale_pos_weight": 1.0, + "n_3d": 50, + "gin": false, + "minimol_concat": false, + "tdc_name": "Solubility_AqSolDB", + "metric": "mae", + "n_fingerprint": 2563, + "gin_dim": 0, + "minimol_dim": 0, + "n_features": 2613, + "seeds": [ + 1, + 2, + 3, + 4, + 5 + ], + "aggregate": "mean" +} diff --git a/solubility_aqsoldb/match.json b/solubility_aqsoldb/match.json new file mode 100644 index 0000000000000000000000000000000000000000..551fe1c0260b840a8d12ca24514dcd7b5e77a5c6 --- /dev/null +++ b/solubility_aqsoldb/match.json @@ -0,0 +1,77 @@ +{ + "endpoint": "solubility_aqsoldb", + "status": "match", + "atol": 1e-05, + "spec": { + "endpoint": "solubility_aqsoldb", + "task": "regression", + "log_scale": false, + "tree": { + "depth": 6, + "iterations": 1000, + "learning_rate": 0.1, + "l2_leaf_reg": 10.0 + }, + "blend_weight": 0.2, + "scale_pos_weight": 1.0, + "n_3d": 50, + "gin": false, + "minimol_concat": false, + "tdc_name": "Solubility_AqSolDB", + "metric": "mae", + "n_fingerprint": 2563, + "gin_dim": 0, + "minimol_dim": 0, + "n_features": 2613, + "seeds": [ + 1, + 2, + 3, + 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