Physics-Informed Neural Networks Modestly Improve Drug Synthesizability Predictions Beyond Training Data
Researchers added physics-based auxiliary losses to a graph neural network (GNN) synthesizability filter and found small but statistically significant improvements in out-of-distribution (OOD) generalization. The study trained on drug-like molecules and tested on natural products, using a 65,177-molecule corpus across five random seeds. The findings suggest that cheap physical priors can partially address a known weakness of purely statistical synthesizability filters used in AI-driven drug discovery.
A preprint posted to arXiv presents a study examining whether physics-derived auxiliary supervision can improve the OOD generalization of a graph neural network synthesizability filter used in machine-learning drug-discovery pipelines. The researchers augmented a GINE backbone with two auxiliary losses: a topological complexity regression term supervised by the Bertz index, and a strain-energy soft penalty supervised by MMFF94 force-field energies. Using a 65,177-molecule dataset drawn from HIV, Tox21, and COCONUT sources and labeled via SAScore thresholds, they conducted a four-way ablation across five random seeds with paired bootstrap confidence intervals. All three physics-aware variants outperformed the baseline (mean OOD AUC 0.9774) in the OOD regime, with the combined model achieving the largest improvement (ΔAUC = +0.0066, 95% CI [+0.0038, +0.0093]). Crucially, the improvements were invisible in-distribution, appearing only under OOD evaluation, and the authors explicitly caution that a single-seed version of the experiment produced a qualitatively different, non-monotone result that did not survive multi-seed testing — underscoring the importance of robust evaluation practices.
What's missing
The study relies on SAScore thresholds as ground-truth synthesizability labels, a proxy measure that is itself a heuristic and may not reflect true experimental synthesizability. The OOD split is limited to a single domain shift (drug-like molecules to natural products), so generalizability to other OOD scenarios — such as de novo generative model outputs — remains untested. The authors do not benchmark against other physics-augmented or hybrid methods beyond the four ablation variants.
What different sources said
- arXiv q-bioCenter
Physics-Aware Auxiliary Losses Improve Out-of-Distribution Generalization of a GNN Synthesizability Filter
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