Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning

A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (−)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targets of (−)-galantamine, with different polypharmacological profiles. Two of the computer-generated hits possess an expanded spectrum of bioactivity on targets relevant to the treatment of Alzheimer's disease and are suitable for hit-to-lead expansion. These results advocate multitarget drug design by advanced virtual screening protocols based on chemically informed machine learning models.