Rastgele Bölmelerden Görülmemiş Çözücülere: Kuantum-Kimya Türevli Yüzey Tanımlayıcıları Çözünürlük Tahminini İyileştiriyor

Özet (abstract)

Abstract Solvent screening often requires predictions for solvents that are absent from the training data. Using 103,262 aligned BigSolDB v2.1 records, we constructed a fixed solvent-level OOD benchmark in which five solvent identities were excluded from model training, validation, early stopping, SHAP ranking, and feature selection. A compact RDKit two-dimensional baseline reaches an IID R2 = 0.954 ± 0.001 under random splitting but falls to an OOD R2 = 0.541 ± 0.019 on the held-out solvents. Under the same benchmark, adding Gasteiger charge proxies and monomer-level GFN2-xTB/COSMO σ-moment surface descriptors raises the OOD R2 to 0.756 ± 0.007. A validation-selected 47-feature representation that also includes deterministic solute–solvent compatibility features reaches an OOD R2 = 0.788 ± 0.008, an MAE = 0.413, and an RMSE = 0.574 log10 S unit under the 20-seed protocol. The improvement depends on solvent identity: DMSO, NMP, and cyclohexane show the largest error reductions, ethylene glycol changes little, and n-propyl acetate is slightly worse than the electrostatic baseline. Learner controls and pair-xTB calculations are used as supporting checks; optimized 1:1 dimers and small 1:2/2:1 clusters do not improve the fixed-OOD result. MICA v0.2 distributes the frozen 20-seed ensemble for prepared-descriptor inference with ensemble-disagreement summaries, applicability-domain flags, and solvent-ranking reports.

Yazarların özeti; kaynağından alınmıştır. Journal of Chemical Information and Modeling, 2026 · DOI ↗