期刊
CHEMMEDCHEM
卷 14, 期 12, 页码 1129-1134出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/cmdc.201900097
关键词
Alzheimer's disease; polypharmacology; scaffold hopping; target prediction; virtual screening
资金
- Swiss National Science Foundation [IZSEZ0 177477]
- ETH Zurich Postdoctoral Fellowship [16-2 FEL-07]
- Swiss National Science Foundation (SNF) [IZSEZ0_177477] Funding Source: Swiss National Science Foundation (SNF)
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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