AI & Machine Learning

Predicting Activity Cliffs for Autonomous Medicinal Chemistry

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Researchers have developed a model that predicts activity cliffs in medicinal chemistry by identifying positions where small structural changes significantly impact drug potency. This advancement helps chemists focus their experiments more efficiently, reducing the number of positions they need to explore by 31%. The model's open-source release and interactive web application make it accessible for broader use in drug discovery.

Read the full article at arXiv cs.LG (ML)

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