SolvAI learns reusable solvent-response coordinates from calculated, empirical and corrected solvation data and predicts hydration free energy directly from molecular structure—without running simulation at inference.
The released system maps one SMILES string to one hydration free energy. Its response surrogates were trained on benchmark-disjoint quantum-continuum, alchemical, empirical and conformational data; the expensive source calculations are not rerun for a query. PIMD-derived features were tested but are not present in the final model.
On the 85-solute neutral-hydration reference set introduced with ARROW:
| Method | Simulation at inference? | MAE (kcal/mol) |
|---|---|---|
| Classical ARROW | yes | 0.785 |
| ARROW/PIMD8 | yes | 0.205 |
| Matched structure-only endpoint | no | 0.303 |
| SolvAI, fixed five-fold OOF | no | 0.202 |
| SolvAI, five complete partitions | no | 0.207 ± 0.004 |
| SolvAI, no ARROW labels in training | no | 0.257 |
The matched endpoint uses exactly the same experimental labels, descriptors, ExtraTrees architecture, weights, folds and seeds; only the 15 response priors are removed. The paired OOF improvement is −0.101 kcal/mol (95% bootstrap interval, −0.215 to −0.020). Shuffled priors do not improve the endpoint, and the advantage survives global family, scaffold, molecular-cluster and nearest-neighbour exclusions.
The supported conclusion is PIMD8-level accuracy on this reference chemistry, not a general sub-0.20 claim. Global family and scaffold separation remain harder at 0.468 and 0.376 kcal/mol, respectively.
Python 3.11, uv and Git LFS are required.
git lfs install
git clone https://github.com/Scientific-Computing-Lab/SolvAI.git
cd SolvAI
make setup
uv run solvai predict 'CCO'The command returns the ensemble-mean hydration free energy and ensemble spread in kcal/mol:
CCO -5.012566 0.004714
The API is equally small:
from solv_ai import predict_smiles
prediction, spread = predict_smiles(["CCO", "c1ccccc1"])make test && make verify && make figures && make paperThis quick path uses frozen, hash-verified artifacts to recompute predictions,
metrics, tables, figures and PDFs. It does not rerun physical calculations or model
training. The preregistered confirmation protocol is in
release/CONFIRMATORY_FREEZE.md, with results in
reports/CONFIRMATORY_ANALYSIS.md.
The compiled manuscript,
Supplementary Information, standalone
Extended Data and machine-readable Supplementary Data are included. See
repro/QUICK_REPRODUCTION.md,
repro/FULL_REPRODUCTION.md and
repro/DATA_PROVENANCE.md.
- Exact and standardized benchmark equivalents are absent from all supervised external training sources used by the confirmatory model.
- Every reported accuracy value is held out; the all-data deployment refit is never used as evidence.
- Shuffled-prior, global chemical-separation and zero-ARROW-label controls are included molecule by molecule.
- Inference requires no experimental target, family/scaffold label, MD, PIMD, ARROW trajectory, probe or routing policy.
- The released artifact contains no retained PIMD-trained feature.
solv_ai/— SMILES-only inference and metric codemodels/final/— standardized-exclusion response surrogates and endpoint ensembleresults/confirmatory/— preregistered predictions, comparisons and statisticsaudits/confirmatory/— identity, similarity and refit auditspaper/— Nature Communications manuscript, Extended Data and Supplementary filesrepro/— quick/full reproduction and data provenance
Citation metadata are provided in CITATION.cff. Code is MIT licensed; external
datasets retain the terms listed in the provenance record.