Improving the accuracy and generalizability of molecular property regression models with a substructure-substitution-rule-informed framework

1.877, respectively, representing 2.6-33.3% performance improvements. We show that both the number and the quality of SSRs contribute to the magnitude of prediction accuracy gains obtained upon adding MolRuleLoss to an MPRM. MolRuleLoss yielded relative improvements on activity-cliff molecules and on a melting-point property-OOD task; however, the absolute prediction error in the OOD setting remained high, indicating that property-OOD generalization remains an open challenge. In a controlled molecular-weight extrapolation experiment, MolRuleLoss reduced the test RMSE of a GEM model from 29.507 to 0.007; because molecular weight is an exact linear function of atomic composition, this result should not be extrapolated to noisy experimental properties. We also provide a formal demonstration that the upper bound of the variation for property change of SSRs are positively correlated with an MPRM's error. Together, we show that using the MolRuleLoss framework as a bolt-on boosts the prediction accuracy and generalizability of multiple MPRMs, supporting diverse applications in areas like cheminformatics and AI-aided drug discovery.