---
license: mit
library_name: protonator
tags:
- chemistry
- cheminformatics
- pka
- logp
- logs
- solubility
- tmelt
- melting-point
- dmpnn
- molecular-property-prediction
---

# protonator-models

Minimal-dependency (torch + rdkit + numpy) D-MPNN model weights for
[protonator](https://github.com/isayevlab/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`.

| Folder | Endpoint | Accuracy |
|---|---|---|
| [`pka_mpnn/`](#pka_mpnn--per-site-pka) | microscopic (per-site) aqueous pKa | scaffold-5CV RMSE ~1.30, random-5CV ~1.08; Enamine external RMSE **0.55** (R² 0.96) |
| [`logp_mpnn/`](#logp_mpnn--octanolwater-logp) | octanol–water logP | 5-fold CV RMSE 0.77, MAE 0.50, R² 0.86 |
| [`logs_mpnn/`](#logs_mpnn--aqueous-logs) | aqueous logS (log₁₀ mol/L, ~298 K) | 5-fold CV RMSE 0.54, MAE 0.35, R² 0.92 |
| [`tmelt_mpnn/`](#tmelt_mpnn--melting-temperature) | melting temperature T_m (Kelvin) | scaffold-5CV RMSE **33.0 K**, MAE 25.3 (R² 0.73); never-trained held-out RMSE 34.0 (Tetko subset 29.5) |

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).

---

## `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):

| Split | RMSE | MAE |
|---|---|---|
| scaffold 5-fold CV | ~1.30 | ~0.95 |
| random 5-fold CV | ~1.08 | — |

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

| | RMSE | MAE | R² |
|---|---|---|---|
| Enamine fluoro | **0.55** | 0.40 | 0.96 |

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

| Probe | this model | prior PKaGIN |
|---|---|---|
| Hammett ρ (para-benzoic series; target 1.00) | **→ 1.0** | 0.34 (95% CI [−0.00, 0.77]) |
| fluoro matched-pair Δ sign-accuracy | **1.00** | 0.33 |

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.

---

## `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

| Split | RMSE (K) | MAE (K) | R² |
|---|---|---|---|
| scaffold 5-fold CV (cleaned labels) | 33.0 | 25.3 | 0.73 |
|   — Tetko subset | 31.8 | — | — |
| never-trained held-out (25k) | 34.0 | 23.9 | 0.65 |
|   — Tetko subset | 29.5 | — | — |

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.

---

## Usage

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

```python
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).

| 5-fold CV (out-of-fold, 21,214 solute/solvent pairs) |
|---|
| dG_solv RMSE 0.95 / MAE 0.51 / R2 0.978 kcal/mol |

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).