Tautomer-Predictor AI Tool Identifies Stable Molecules for Drug Discovery
Researchers 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. The post Tautomer-Predictor AI Tool Identifies Stable Molecules for Drug Discovery appeared first on GEN - Genetic Engineering and Biotechnology News .
New York University researchers have developed an AI tool, Tautomer-Predictor, to rapidly and accurately predict the stable forms of drug-like molecules called tautomers. Tautomerism poses a significant challenge in molecular design and drug discovery as incorrect tautomer assignment can compromise various tasks, including molecular docking and virtual screening.
The research, led by NYU chemistry professor Yingkai Zhang, addresses this issue by utilizing high-resolution small-molecule X-ray crystal structures from the Cambridge Structural Database, which contain experimentally resolved hydrogen positions. By training a graph neural network on this data, the AI model can predict stable tautomers directly from 2D molecular forms, offering a more feasible and efficient solution compared to existing methods.
In a study applying the model to 5,075 PDBbind ligands, the researchers found that approximately 2.5% of the assigned tautomers were likely incorrect, with the model suggesting alternative stable structures that improved hydrogen bonding patterns.
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