Novel Predictive Model for Explaining How Anti-Fibrotic Drugs Work

Dr. Jeff Saucerman

In drug discovery, the focus of machine learning and artificial intelligence tools has been on predicting outcomes without explanation or understanding of the biochemical pathways mediating those effects. But rigorous translation requires a solid science-based foundation to explain how a drug works, not just that it does, according to Jeff Saucerman, PhD, Professor of Biomedical Engineering and Cardiovascular Medicine at the University of Virginia. To that end, Saucerman and his colleagues created a logic-based mechanistic machine learning (LogiMML) approach combining the strengths of machine learning with a previously developed mechanistic network model of the signaling that happens in cardiac fibroblasts. The behavior of the mechanistic model has been validated in hundreds of conditions and aided the design of later basic science experiments, he says. 

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