Literature DB >> 31629547

Has Drug Design Augmented by Artificial Intelligence Become a Reality?

Hongming Chen1, Ola Engkvist2.   

Abstract

The application of artificial intelligence (AI) to drug discovery has become a hot topic in recent years. Generative molecular design based on deep learning is a particular an area of attention. Zhavoronkov et al. recently published a novel approach in which de novo molecular design based on deep learning was used to discover novel potent DDR1 kinase inhibitors. It took 21 days from model building to compound design, and a total of six AI-designed compounds were synthesized and tested. The study highlights how quickly the field of AI-designed compounds is developing, and we can expect further developments in the coming years.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  DDR1; GENTRL; deep learning; drug design; drug discovery

Mesh:

Year:  2019        PMID: 31629547     DOI: 10.1016/j.tips.2019.09.004

Source DB:  PubMed          Journal:  Trends Pharmacol Sci        ISSN: 0165-6147            Impact factor:   14.819


  5 in total

1.  Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Authors:  Morgan Thomas; Robert T Smith; Noel M O'Boyle; Chris de Graaf; Andreas Bender
Journal:  J Cheminform       Date:  2021-05-13       Impact factor: 5.514

Review 2.  Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Authors:  José T Moreira-Filho; Arthur C Silva; Rafael F Dantas; Barbara F Gomes; Lauro R Souza Neto; Jose Brandao-Neto; Raymond J Owens; Nicholas Furnham; Bruno J Neves; Floriano P Silva-Junior; Carolina H Andrade
Journal:  Front Immunol       Date:  2021-05-31       Impact factor: 7.561

3.  Quantum-mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

Authors:  Nicolas Tielker; Lukas Eberlein; Gerhard Hessler; K Friedemann Schmidt; Stefan Güssregen; Stefan M Kast
Journal:  J Comput Aided Mol Des       Date:  2020-10-20       Impact factor: 3.686

4.  Fine-tuning of a generative neural network for designing multi-target compounds.

Authors:  Thomas Blaschke; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2021-05-28       Impact factor: 4.179

5.  Compound dataset and custom code for deep generative multi-target compound design.

Authors:  Thomas Blaschke; Jürgen Bajorath
Journal:  Future Sci OA       Date:  2021-04-30
  5 in total

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