Literature DB >> 29774657

Cheminformatics in Drug Discovery, an Industrial Perspective.

Hongming Chen1, Thierry Kogej1, Ola Engkvist1.   

Abstract

Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high-throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions.
© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Cheminformatics; High-Throughput Screening; Library Design; Machine Learning

Mesh:

Year:  2018        PMID: 29774657     DOI: 10.1002/minf.201800041

Source DB:  PubMed          Journal:  Mol Inform        ISSN: 1868-1743            Impact factor:   3.353


  7 in total

1.  Editorial: Chemoinformatics Approaches to Structure- and Ligand-Based Drug Design, Volume II.

Authors:  Leonardo L G Ferreira; Adriano D Andricopulo
Journal:  Front Pharmacol       Date:  2022-06-29       Impact factor: 5.988

2.  SMILES-based deep generative scaffold decorator for de-novo drug design.

Authors:  Josep Arús-Pous; Atanas Patronov; Esben Jannik Bjerrum; Christian Tyrchan; Jean-Louis Reymond; Hongming Chen; Ola Engkvist
Journal:  J Cheminform       Date:  2020-05-29       Impact factor: 5.514

3.  A de novo molecular generation method using latent vector based generative adversarial network.

Authors:  Oleksii Prykhodko; Simon Viet Johansson; Panagiotis-Christos Kotsias; Josep Arús-Pous; Esben Jannik Bjerrum; Ola Engkvist; Hongming Chen
Journal:  J Cheminform       Date:  2019-12-03       Impact factor: 5.514

4.  Expanding the drug discovery space with predicted metabolite-target interactions.

Authors:  Andrea Nuzzo; Somdutta Saha; Ellen Berg; Channa Jayawickreme; Joel Tocker; James R Brown
Journal:  Commun Biol       Date:  2021-03-05

Review 5.  Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs.

Authors:  Sharna-Kay Daley; Geoffrey A Cordell
Journal:  Molecules       Date:  2021-06-22       Impact factor: 4.411

Review 6.  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

7.  Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders.

Authors:  Esben Jannik Bjerrum; Boris Sattarov
Journal:  Biomolecules       Date:  2018-10-30
  7 in total

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