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Xia, Z. ; Karpov, P. ; Popowicz, G.M. ; Tetko, I.V.

Focused library generator: Case of Mdmx inhibitors.

J. Comput.-Aided Mol. Des. 34, 769–782 (2020)
Postprint DOI PMC
Open Access Green
We present a Focused Library Generator that is able to create from scratch new molecules with desired properties. After training the Generator on the ChEMBL database, transfer learning was used to switch the generator to producing new Mdmx inhibitors that are a promising class of anticancer drugs. Lilly medicinal chemistry filters, molecular docking, and a QSAR IC50 model were used to refine the output of the Generator. Pharmacophore screening and molecular dynamics (MD) simulations were then used to further select putative ligands. Finally, we identified five promising hits with equivalent or even better predicted binding free energies and IC50 values than known Mdmx inhibitors. The source code of the project is available on https://github.com/bigchem/online-chem.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Lstm ; Mdmx Inhibitors ; Molecular Dynamics ; Pharmacophore ; Structure Generation ; Transfer Learning; P53; Database; Protein; Identification; Discovery; Optimization; Methodology; Molecules; Chemistry; Accuracy
Sprache englisch
Veröffentlichungsjahr 2020
Prepublished im Jahr 2019
HGF-Berichtsjahr 2019
ISSN (print) / ISBN 0920-654X
e-ISSN 1573-4951
Zeitschrift Journal of Computer-Aided Molecular Design
Quellenangaben Band: 34, Heft: , Seiten: 769–782 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503000-001
DOI 10.1007/s10822-019-00242-8
WOS ID WOS:000493643800001
Scopus ID 85075811806
PubMed ID 31677002
Erfassungsdatum 2019-11-05
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