Generative AI on track to shape the future of drug design
Using advanced artificial intelligence, researchers have developed a novel method to make drug development faster and more efficient.
In a new paper, Xia Ning, PhD, lead author of the study and a professor of biomedical informatics and computer science and engineering at The Ohio State University, introduces DiffSMol, a generative AI model capable of generating realistic 3D structures of small molecules that can serve as promising drug candidates.
DiffSMol works by analyzing the shapes of known ligands – molecules that bind to protein targets – and using these shapes as conditions to generate novel 3D molecules that better bind to the protein targets. Study results showed that when used to create molecules with the potential to quicken the drug-making process, DiffSmol has a 61.4% success rate, outperforming prior research attempts that achieved success about 12% of the time.
“By using well-known shapes as a condition, we can train our model to generate novel molecules with similar shapes that don’t exist in previous chemical databases,” said Ning.
