Literature DB >> 30730601

Automated De Novo Drug Design: Are We Nearly There Yet?

Gisbert Schneider1, David E Clark2.   

Abstract

Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.
© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  artificial intelligence; drug discovery; machine learning; medicinal chemistry; synthesis

Mesh:

Year:  2019        PMID: 30730601     DOI: 10.1002/anie.201814681

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  15 in total

1.  Systemic evolutionary chemical space exploration for drug discovery.

Authors:  Chong Lu; Shien Liu; Weihua Shi; Jun Yu; Zhou Zhou; Xiaoxiao Zhang; Xiaoli Lu; Faji Cai; Ning Xia; Yikai Wang
Journal:  J Cheminform       Date:  2022-04-01       Impact factor: 5.514

2.  Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.

Authors:  Rocco Meli; Garrett M Morris; Philip C Biggin
Journal:  Front Bioinform       Date:  2022-06-17

Review 3.  Trends in application of advancing computational approaches in GPCR ligand discovery.

Authors:  Siyu Zhu; Meixian Wu; Ziwei Huang; Jing An
Journal:  Exp Biol Med (Maywood)       Date:  2021-02-27

4.  EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation.

Authors:  Jules Leguy; Thomas Cauchy; Marta Glavatskikh; Béatrice Duval; Benoit Da Mota
Journal:  J Cheminform       Date:  2020-09-16       Impact factor: 5.514

Review 5.  Pyrazolone structural motif in medicinal chemistry: Retrospect and prospect.

Authors:  Zefeng Zhao; Xufen Dai; Chenyang Li; Xiao Wang; Jiale Tian; Ying Feng; Jing Xie; Cong Ma; Zhuang Nie; Peinan Fan; Mingcheng Qian; Xirui He; Shaoping Wu; Yongmin Zhang; Xiaohui Zheng
Journal:  Eur J Med Chem       Date:  2019-11-16       Impact factor: 6.514

6.  Structural and Functional Enrichment Analyses for Antimicrobial Peptides.

Authors:  Sheng C Lo; Zhong-Ru Xie; Kuan Y Chang
Journal:  Int J Mol Sci       Date:  2020-11-20       Impact factor: 5.923

7.  Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex.

Authors:  Pascal Friederich; Gabriel Dos Passos Gomes; Riccardo De Bin; Alán Aspuru-Guzik; David Balcells
Journal:  Chem Sci       Date:  2020-04-07       Impact factor: 9.825

Review 8.  In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Authors:  Lauro Ribeiro de Souza Neto; José Teófilo Moreira-Filho; Bruno Junior Neves; Rocío Lucía Beatriz Riveros Maidana; Ana Carolina Ramos Guimarães; Nicholas Furnham; Carolina Horta Andrade; Floriano Paes Silva
Journal:  Front Chem       Date:  2020-02-18       Impact factor: 5.221

Review 9.  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

10.  Synthetic Activators of Cell Migration Designed by Constructive Machine Learning.

Authors:  Dominique Bruns; Daniel Merk; Karthiga Santhana Kumar; Martin Baumgartner; Gisbert Schneider
Journal:  ChemistryOpen       Date:  2019-10-23       Impact factor: 2.911

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