Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors.
artificial intelligence
crystal structure
de novo design
drug discovery
neural network
Journal
Molecular informatics
ISSN: 1868-1751
Titre abrégé: Mol Inform
Pays: Germany
ID NLM: 101529315
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
08
05
2020
accepted:
17
06
2020
entrez:
15
1
2021
pubmed:
16
1
2021
medline:
24
8
2021
Statut:
ppublish
Résumé
Ligand-based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label-free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim-1 kinase. The three-dimensional crystal structure complex of human Pim-1 with ligand bound revealed an ATP-competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity-based and generative computational approaches.
Identifiants
pubmed: 33448694
doi: 10.1002/minf.202000109
pmc: PMC7539333
doi:
Substances chimiques
Ligands
0
Protein Kinase Inhibitors
0
PIM1 protein, human
EC 2.7.11.1
Proto-Oncogene Proteins c-pim-1
EC 2.7.11.1
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2000109Informations de copyright
© 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
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