Abstract
Tautomerization plays a critical role in chemical and biological processes, influencing molecular stability, reactivity, biological activity, and ADME-Tox properties. Many drug-like molecules exist in multiple tautomeric states in aqueous solution, complicating the study of protein-ligand interactions. Rapid and accurate prediction of tautomer ratios and identification of predominant species are therefore crucial in computational drug discovery. In this study, we introduce sPhysNet-Taut, a deep learning model fine-tuned on experimental data using a Siamese neural network architecture. This model directly predicts tautomer ratios in aqueous solution based on MMFF94-optimized molecular geometries. On experimental test sets, sPhysNet-Taut achieves state-of-the-art performance with root-mean-square error (RMSE) of 1.9 kcal/mol on the 100-tautomers set and 1.0 kcal/mol on the SAMPL2 challenge, outperforming all other methods. It also provides superior ranking power for tautomer pairs on multiple test sets. Our results demonstrate that fine-tuning on experimental data significantly enhances model performance compared to training from scratch. This work not only offers a valuable deep learning model for predicting tautomer ratios but also presents a protocol for modeling pairwise data. To promote usability, we have developed an accessible tool that predicts stable tautomeric states in aqueous solution by enumerating all possible tautomeric states and ranking them using our model. The source code and web server are freely accessible at https://github.com/xiaolinpan/sPhysNet-Taut and https://yzhang.hpc.nyu.edu/tautomer.