| Literature DB >> 31677002 |
Zhonghua Xia1, Pavel Karpov1,2, Grzegorz Popowicz1, Igor V Tetko3,4.
Abstract
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.Entities:
Keywords: LSTM; Mdmx inhibitors; Molecular dynamics; Pharmacophore; Structure generation; Transfer learning
Year: 2019 PMID: 31677002 DOI: 10.1007/s10822-019-00242-8
Source DB: PubMed Journal: J Comput Aided Mol Des ISSN: 0920-654X Impact factor: 3.686