Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures

Accurate prediction of aqueous solubility remains a fundamental challenge in drug discovery and molecular design, with traditional machine learning approaches often producing predictions that violate basic thermodynamic principles. Here, we introduce a physics-informed neural network (PINN) framework that explicitly incorporates thermodynamic constraints into the learning process, ensuring physical consistency while improving predictive accuracy. Our approach decomposes solvation free energy into physically interpretable components—cavity formation, electrostatic interactions, van der Waals forces, and hydrogen bonding—and enforces their thermodynamic relationships through custom loss functions. We augment the standard AqSolDB dataset (9982 molecules) with quantum chemistry calculations for 1,500 molecules, providing ground-truth energy decompositions. The physics-informed model achieves RMSE of 0.36 log units on AqSolDB and demonstrates 35% improved performance on scaffold split extrapolation on extrapolation to novel chemical scaffolds compared to purely data-driven approaches. Critically, 96.3% of predictions satisfy stringent thermodynamic consistency (energy conservation error < 2 kJ/mol), compared to only 67.4% for standard neural networks, with major physics violations reduced from 32.6 to 3.7%. This work establishes a general framework for incorporating domain knowledge into molecular property prediction, with immediate applications in drug design, materials discovery, and chemical engineering.