Literature DB >> 31194543

Deep Learning in Chemistry.

Adam C Mater1, Michelle L Coote1.   

Abstract

Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry to drug and materials design and even synthesis planning. This review aims to explain the concepts of deep learning to chemists from any background and follows this with an overview of the diverse applications demonstrated in the literature. We hope that this will empower the broader chemical community to engage with this burgeoning field and foster the growing movement of deep learning accelerated chemistry.

Keywords:  Cheminformatics; Computational chemistry; Deep learning; Drug design; Machine learning; Materials design; Open sourcing; Quantum mechanical calculations; Representation learning; Synthesis planning

Mesh:

Year:  2019        PMID: 31194543     DOI: 10.1021/acs.jcim.9b00266

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  37 in total

Review 1.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

2.  Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning.

Authors:  Alexander A Vinogradov; Jun Shi Chang; Hiroyasu Onaka; Yuki Goto; Hiroaki Suga
Journal:  ACS Cent Sci       Date:  2022-05-26       Impact factor: 18.728

3.  Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling.

Authors:  Christopher W Smith; Mustafa Salih Hizir; Nidhi Nandu; Mehmet V Yigit
Journal:  Anal Chem       Date:  2021-12-29       Impact factor: 8.008

Review 4.  Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning.

Authors:  Dongdong Zhang; Song Xia; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2022-04-14       Impact factor: 6.162

Review 5.  Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future.

Authors:  Andrew F Zahrt; Soumitra V Athavale; Scott E Denmark
Journal:  Chem Rev       Date:  2019-12-30       Impact factor: 60.622

6.  TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Authors:  Qiao Liu; Lei Xie
Journal:  PLoS Comput Biol       Date:  2021-02-12       Impact factor: 4.475

7.  Graph Convolutional Network-Based Screening Strategy for Rapid Identification of SARS-CoV-2 Cell-Entry Inhibitors.

Authors:  Peng Gao; Miao Xu; Qi Zhang; Catherine Z Chen; Hui Guo; Yihong Ye; Wei Zheng; Min Shen
Journal:  J Chem Inf Model       Date:  2022-04-11       Impact factor: 6.162

Review 8.  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

9.  Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics.

Authors:  Raquel Rodríguez-Pérez; Jürgen Bajorath
Journal:  Sci Rep       Date:  2021-07-09       Impact factor: 4.379

Review 10.  Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning.

Authors:  Gennady M Verkhivker; Steve Agajanian; Guang Hu; Peng Tao
Journal:  Front Mol Biosci       Date:  2020-07-09
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