ailab-bio/gnn_synth

PROTAC Synthesizability — CheMeleon GNN

A graph neural network that predicts the heavy-atom-count weighted synthesizability score (hac_weighted_score) of PROTAC molecules from SMILES. Built on the CheMeleon foundation model (a pretrained D-MPNN) fine-tuned via ChemProp — graph-only, no engineered features.

Nested 5×5 scaffold cross-validation, Optuna tuning. Mean CV R² = 0.643.

Files

• gnn_v3_final.ckpt — fine-tuned model checkpoint (weights + target scaler)
• gnn_v3_hparams.yaml — hyperparameters

Usage

Requires the project code: https://github.com/ribesstefano/PROTAC-Synthesizability

from gnn.model import CheMeleonRegressor

model  = CheMeleonRegressor.load("gnn_v3_final")   # base path, no extension
smiles = ["O=C(O)c1ccccc1"]
preds  = model.predict(smiles)                     # graph-only: SMILES in, prediction out

Dependencies

Pin these for reproducible loading (RDKit featurization is version-sensitive): chemprop>=2.2.0, lightning, torch, rdkit, numpy.

License

MIT