Add 22 endpoint CatBoost checkpoints

.gitattributes

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README.md

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+---
+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.<name>.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
+
+```
+<endpoint>/config.json
+<endpoint>/match.json                       # audit log only
+<endpoint>/seed{1-5}/catboost.cbm
+<endpoint>/seed{1-5}/y_scaler.json          # regression only
+<endpoint>/seed{1-5}/minimol_head.npz       # blend_weight > 0 (preferred)
+<endpoint>/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. Their pretrained weights are not included in this repository.

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+  "endpoint": "ames",
+  "task": "classification",
+  "log_scale": false,
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+    "iterations": 2000,
+    "learning_rate": 0.03,
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+  "tdc_name": "AMES",
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+  ],
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+}

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+  "endpoint": "bioavailability_ma",
+  "task": "classification",
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