Physics-Informed Neural Networks Modestly Improve Drug Synthesizability Predictions Beyond Training Data
Researchers added physics-based auxiliary losses to a graph neural network to improve predictions of whether proposed drug molecules can be synthesized, testing on molecules outside the training distribution. The approach showed small but statistically significant improvements (0.3–0.7% AUC gain) when evaluated on out-of-distribution natural products, though effects were invisible on in-distribution data. The finding matters because generative drug-discovery models increasingly propose novel molecules that existing synthesizability filters struggle to evaluate accurately.
A preprint study evaluated whether incorporating physical priors—topological complexity (Bertz index) and strain energy (MMFF94 force-field)—as auxiliary supervision could help a graph neural network generalize better when assessing synthesizability of molecules outside its training distribution. The researchers trained on 65,177 molecules labeled by existing filters (SAScore, SCScore, RAscore, DeepSA) and tested on a held-out set of natural products. All three physics-aware variants outperformed the baseline in the out-of-distribution regime, with the combined approach achieving a +0.0066 AUC improvement (95% CI [+0.0038, +0.0093]). Critically, the authors report a methodological caution: a single-seed version of the experiment produced qualitatively different results that did not replicate across five seeds, highlighting the importance of multi-seed evaluation. The improvements are modest but consistent and statistically significant, and the variants showed no advantage on in-distribution data.
What's missing
The study does not discuss computational cost or inference time overhead of the physics-aware variants compared to the baseline, which would be relevant for practical deployment in high-throughput screening pipelines. Additionally, the paper does not explore whether the auxiliary losses transfer to other out-of-distribution scenarios (e.g., different chemical scaffolds or activity classes) beyond the single natural-products test set.
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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