Document tmelt_mpnn (melting-temperature) model + benchmarks

README.md

@@ -8,6 +8,8 @@ tags:
 - logp
 - logs
 - solubility
+- tmelt
+- melting-point
 - dmpnn
 - molecular-property-prediction
 ---
@@ -15,7 +17,7 @@ tags:
 # protonator-models
 
 Minimal-dependency (torch + rdkit + numpy) D-MPNN model weights for
-[protonator](https://github.com/isayevlab/protonator). Each model is a **5-fold ensemble**;
+[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`.
@@ -25,9 +27,11 @@ calibrated uncertainty. Weights are fetched automatically at a pinned revision v
 | [`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_0.pt`…`fold_4.pt` + `config.json` (per-fold output denormalization
-and a featurizer-version contract validated at load).
+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).
 
 ---
 
@@ -90,6 +94,40 @@ D-MPNN, 5-fold ensemble. **5-fold CV: RMSE 0.77, MAE 0.50, R² 0.86.**
 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