Literature DB >> 31677002

Focused Library Generator: case of Mdmx inhibitors.

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


  4 in total

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Journal:  J Cheminform       Date:  2020-03-18       Impact factor: 5.514

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Authors:  Ruud van Deursen; Peter Ertl; Igor V Tetko; Guillaume Godin
Journal:  J Cheminform       Date:  2020-04-10       Impact factor: 5.514

3.  Evaluating Deep Learning models for predicting ALK-5 inhibition.

Authors:  Gabriel Z Espinoza; Rafaela M Angelo; Patricia R Oliveira; Kathia M Honorio
Journal:  PLoS One       Date:  2021-01-28       Impact factor: 3.240

4.  More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins.

Authors:  Aleksey I Rusanov; Olga A Dmitrieva; Nugzar Zh Mamardashvili; Igor V Tetko
Journal:  Int J Mol Sci       Date:  2022-01-21       Impact factor: 5.923

  4 in total

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