What is it about?
Drug-target affinity is an important parameter in pharmaceutical research, and predicting it with computational methods provides great value to researchers. However, rigorous approaches have been slow and complicated. We present a novel method and theory for the prediction of drug target affinity, that is rigorous and accurate, yet fast enough to evaluate thousands of compounds per day, and demonstrate its utility in pharmaceutical drug discovery.
Featured Image
Photo by Omar:. Lopez-Rincon on Unsplash
Why is it important?
Our method accelerates the slow and costly early drug discovery by allowing researchers to make low cost, rapid accurate predictions of drug molecules before expensive experimental synthesis and testing. Unlike most existing methods, ours predicts a molecule's absolute binding affinity rather than just ranking similar compounds, and does so by simulating the molecule physically leaving the target, as occurs in the real biological systems.
Perspectives
I proposed the basic theory (population-reweighting) for this method many years ago as a graduate student. Now I have had the opportunity to work with great co-authors at Alivexis to adapt and build that theory into a workflow that is making a big impact on industrial drug discovery.
William Sinko
Alivexis Inc.
Read the Original
This page is a summary of: ModBind
dG
: A simulation-based absolute predictor of free energy of binding based on population reweighting, Proceedings of the National Academy of Sciences, June 2026, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2513285123.
You can read the full text:
Contributors
The following have contributed to this page
