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Updated: Jan 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning
Francesca Grisoni1, Daniel Merk1, Lukas Friedrich1
1Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
Abstract:
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.
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