protonator-models

protonator-models

Minimal-dependency (torch + rdkit + numpy) D-MPNN model weights for protonator. Each model is an ensemble (5-fold; tmelt_mpnn is a 10-model loss-diverse consensus); protonator returns the mean prediction with the across-fold standard deviation as a calibrated uncertainty. Weights are fetched automatically at a pinned revision via huggingface_hub.

Each folder holds fold_*.pt + config.json (per-fold output denormalization and a featurizer-version contract validated at load); tmelt_mpnn/ ships 10 folds plus a desc_norm.json (descriptor standardization).

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pka_mpnn — per-site pKa

Microscopic (per-ionization-site) aqueous pKa for drug-like small molecules, 2D-only (SMILES / molecular graph; no 3D conformers, no QM). Given a SMILES and an ionization-center (IC) atom, predicts that site's pKa with an ensemble uncertainty.

Architecture (per fold)

• depth-3 directed-bond D-MPNN, hidden 1024
• distance-conditioned IC-centric attention readout (attention+dist): a learned shortest-path-distance bias routes any substituent — at any topological distance — to the ionization center in O(1), so a shallow model is sensitive to remote substituent effects
• inductive descriptor at the IC (Taft σ_I / Swain–Lupton σ_F / Kier–Hall E-state, with no distance cutoff)
• dropout 0.15 + weight-decay; per-fold output denormalization; 5-fold ensemble

Benchmarks

Out-of-fold 5-fold cross-validation on a curated, residual-denoised combination of ChEMBL / iBonD / IUPAC experimental pKa (~17.6k per-site measurements):

Held-out external set (Enamine fluoro, 158 molecules, not used in the external evaluation):

Remote-substituent sensitivity (the headline improvement over the prior PKaGIN model):

The prior model is degenerate on remote substituents (it predicts near-identical pKa for a molecule and an analog whose substituent lies beyond its receptive field); this model fixes that while matching/exceeding overall accuracy.

Required input standardization

The model was trained on neutral, desalted, largest-organic-fragment SMILES that were not tautomer-canonicalized. protonator.predict_sites applies the matching standardization (desalt + largest-fragment + neutralize, no tautomer canonicalization) before detecting ionization centers, so charged species and salts are handled correctly. Do not bypass it for arbitrary user input.

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logp_mpnn — octanol/water logP

D-MPNN, 5-fold ensemble. 5-fold CV: RMSE 0.77, MAE 0.50, R² 0.86.

logs_mpnn — aqueous logS

Aqueous log solubility (log₁₀ mol/L, ~298 K); shares the D-MPNN trunk with logP, trained jointly. 5-fold CV: RMSE 0.54, MAE 0.35, R² 0.92.

tmelt_mpnn — melting temperature

Melting point T_m (Kelvin) for organic small molecules, 2D-only (SMILES / molecular graph; no 3D conformers, no crystal structure). Shares the CheMeleon-initialized D-MPNN trunk with logP/logS (hidden 2048, depth 6, mean aggregation) and adds descriptor infusion: 11 physically-grounded melting-point descriptors (topological symmetry number, conformational flexibility, H-bond donors/acceptors, ring & aromatic rigidity, TPSA, size) are concatenated to the pooled graph encoding before the FFN head. Deployed as a 10-model loss-diverse consensus (MSE + Huber objectives × 5 scaffold folds); desc_norm.json ships the descriptor standardization applied at inference.

Data

Forensically-cleaned 243k-molecule corpus combining a patent-mined set (~214k) and the Tetko/OCHEM literature set (~36k). Multi-signal label QC (cross-validated model residual + structural-neighbor consistency + scaffold consistency), chemist review, and a non-circular drop-validation flagged and removed 6,748 corroborated bad labels (2.7%) — °F↔°C unit errors, boiling/decomposition temperatures recorded as melting points, free-base/salt mismatches, and a Tetko missing-value sentinel — while protecting genuinely high-melting aromatic polyacids.

Benchmarks

Melting point is the hardest of the common physicochemical endpoints (it depends on crystal packing, which a single-molecule 2D graph cannot encode); the experimental inter-source noise floor on this kind of broad-range data is σ ≈ 35 K. ~33–34 K RMSE on trustworthy labels is therefore at the state-of-the-art frontier and matches/edges the best published consensus models on the Tetko benchmark.

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Usage

protonator fetches these automatically (pinned revision). Manual load:

from protonator.ml.models.pka_mpnn import PKaPredictor
pred = PKaPredictor(weights_dir="<pka_mpnn folder>", device="cpu")
sites = pred.predict_sites("[Na+].CC(=O)[O-]")   # auto-standardized -> Carboxylic Acid ~4.96

Accuracy figures are out-of-fold cross-validation on the experimental training data plus a held-out external set; they are not directly comparable across endpoints (different data and splits).

Citation

Isayev lab, protonator — https://github.com/isayevlab/protonator

solvation_mpnn (solvation free energy, dG_solv)

solvation_mpnn/ — solute-in-solvent solvation free energy (dG_solv, kcal/mol at 298.15 K). Dual-encoder D-MPNN: separate solute and solvent encoders (hidden 2048, depth 6, 72-dim atom features) feeding an FFN over both pooled vectors plus per-molecule RDKit SlogP_VSA descriptors (4120 -> 1024 -> 1024 -> 1); 5-fold ensemble. Self-contained (torch + rdkit + numpy only).

Also drives octanol-water LogP and arbitrary phase log-partition coefficients via the thermodynamic cycle (dG_a - dG_b) / RT ln10. ensemble_fold_0.pt..ensemble_fold_4.pt (bare state_dicts) + config.json (informational provenance; architecture is fixed in protonator.ml.models._common.ENCODER_CONFIG, not parsed at load).