When Do Quantum-Chemical Descriptors Improve Aqueous Solubility Prediction? A Residual-Rescue Benchmark under Chemical Distribution Shift
Year: 2026
Type: preprint
Abstract: Quantum-chemical descriptors are increasingly appended to molecular machine-learning representations, yet their computational cost is difficult to justify when the average accuracy gain is small. We asked a stricter question for aqueous solubility: do low-cost quantum descriptors contain information that is genuinely nonredundant with conventional two-dimensional chemistry, and under which chemical regimes is that information useful? A frozen 2,996-molecule internal cohort derived from AqSolDB was evaluated with random, Bemis-Murcko scaffold-disjoint, and Butina-cluster-disjoint splits, and an independent 820-molecule OChemUnseen cohort was reserved for external validation. GFN2-xTB/ALPB-water descriptors produced small but reproducible direct improvements when added to RDKit descriptors (mean internal RMSE reductions 0.009-0.018 logS across model/split combinations; all 10 seeds improved). More importantly, a strict out-of-fold residual-rescue architecture, in which a quantum-only model learned errors left by the 2D model, improved RMSE by 0.028, 0.047, and 0.028 logS under random, scaffold, and cluster splits, respectively (10/10 seeds; paired Wilcoxon p = 0.001953 for each). On the external set, quantum augmentation reduced the 95th-percentile absolute error by 0.055 logS. Frontier-orbital descriptors accounted for most of the external gain, and significant benefits after false-discovery-rate correction were concentrated in nitro, amine, amide, and alcohol/phenol subsets. A learned low-cost gate did not reliably predict which molecules would benefit, providing a useful negative result. These results show that inexpensive quantum descriptors add modest average accuracy but measurable orthogonal information, with the clearest value under chemical distribution shift and in difficult or electronically distinctive cases.
Source: ChemRxiv
Author: Aris Tsai
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7215042103