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Design of Natural‐Product‐Inspired Multitarget Ligands by Machine Learning
Author(s) -
Grisoni Francesca,
Merk Daniel,
Friedrich Lukas,
Schneider Gisbert
Publication year - 2019
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201900097
Subject(s) - virtual screening , computer science , galantamine , natural product , drug discovery , machine learning , artificial intelligence , product (mathematics) , protocol (science) , bioinformatics , chemistry , medicine , disease , dementia , donepezil , biology , mathematics , geometry , alternative medicine , pathology , stereochemistry
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.