New AI tool predicts hydrogen positions to speed up drug discovery
· News-MedicalNew York University researchers have trained an AI model to learn chemical patterns associated with stability in drug-like molecules and accurately predict where their hydrogen atoms should be positioned.
Yingkai Zhang, professor of chemistry at NYU and study's senior authorAlthough this may seem like a small change, different tautomers of the same molecule can alter how a molecule interacts with a protein target. Correct tautomer assignment is consequently important for molecular modeling and structure-based drug discovery."
Yet determining the correct tautomer remains a challenge-like finding a needle in a moving haystack. This is, in part, because of a scarcity of experimental data that characterizes tautomer structures. For instance, in the Protein Data Bank, a worldwide repository of the 3D structures of large biological molecules that is widely used in biological research, the location of hydrogen atoms that distinguish one tautomer from another are typically not available. The Protein Data Bank contains molecular structures that are largely determined using experimental methods such as X-ray crystallography, but the resolution of macromolecular X-ray structures is generally insufficient to locate the position of hydrogen atoms reliably. As a result, hydrogen positions-and therefore the tautomeric state of a molecule-often have to be inferred by scientists.
Other methods also fall short. Using quantum mechanics to assess tautomers is often too computationally demanding and expensive for screening full libraries, while machine learning is limited by the relatively small datasets of experimentally characterized tautomers in solution, which tend to contain only a few hundred molecules.
"We realized that experimentally resolved hydrogen positions in high-resolution small-molecule crystal structures provide a largely untapped source of experimental information about tautomer stability," said Zhang, who is also part of the NYU Simons Center for Computational Physical Chemistry.
Xiaolin Pan, a postdoc in Zhang's lab and the study's first author, systematically mined the Cambridge Structural Database to construct a dataset containing more than 1.1 million tautomeric states, orders of magnitude larger than existing experimental datasets. He then trained a graph neural network-a type of artificial intelligence that uses deep learning to find patterns between connected data points-to predict stable tautomers directly from 2D molecular forms, without requiring 3D structures or quantum-mechanical calculations.
For instance, in one of the molecules in the Protein Data Bank, the AI model predicted a tautomer with a differently positioned hydrogen atom than in the original assignment. As a result of this change, the ligand forms additional hydrogen bonding with nearby protein residues-illustrating how a seemingly small change can alter the molecule's interactions with its protein environment.
"These calculations require the molecule's hydrogen positions and chemical bonding to be correctly assigned, and using the wrong tautomer could alter the predicted interactions and dynamics," said Zhang.
Additional study authors include Chao Han and Fengyang Han of NYU's Department of Chemistry. This research is published in Chemical Science, the Royal Society of Chemistry's peer-reviewed flagship journal, and is free to read.
This work was supported by the National Institutes of Health (R35-GM127040). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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