Literature DB >> 27491648

Deep Learning in Drug Discovery.

Erik Gawehn1, Jan A Hiss1, Gisbert Schneider2.   

Abstract

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.
© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  bioinformatics; cheminformatics; drug design; machine-learning; neural network; virtual screening

Mesh:

Year:  2015        PMID: 27491648     DOI: 10.1002/minf.201501008

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


  94 in total

Review 1.  Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

Authors:  Linlin Zhao; Heather L Ciallella; Lauren M Aleksunes; Hao Zhu
Journal:  Drug Discov Today       Date:  2020-07-11       Impact factor: 7.851

2.  Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk.

Authors:  S Jafar Ali Ibrahim; M Thangamani
Journal:  J Med Syst       Date:  2018-09-01       Impact factor: 4.460

3.  Deep learning in biomedicine.

Authors:  Michael Wainberg; Daniele Merico; Andrew Delong; Brendan J Frey
Journal:  Nat Biotechnol       Date:  2018-09-06       Impact factor: 54.908

4.  Identification of small molecule inhibitors of ALK2: a virtual screening, density functional theory, and molecular dynamics simulations study.

Authors:  Tasneem Kausar; Shahid M Nayeem
Journal:  J Mol Model       Date:  2018-08-29       Impact factor: 1.810

Review 5.  Computational polypharmacology: a new paradigm for drug discovery.

Authors:  Rajan Chaudhari; Zhi Tan; Beibei Huang; Shuxing Zhang
Journal:  Expert Opin Drug Discov       Date:  2017-01-23       Impact factor: 6.098

Review 6.  Automating drug discovery.

Authors:  Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2017-12-15       Impact factor: 84.694

Review 7.  Deep learning in pharmacogenomics: from gene regulation to patient stratification.

Authors:  Alexandr A Kalinin; Gerald A Higgins; Narathip Reamaroon; Sayedmohammadreza Soroushmehr; Ari Allyn-Feuer; Ivo D Dinov; Kayvan Najarian; Brian D Athey
Journal:  Pharmacogenomics       Date:  2018-04-26       Impact factor: 2.533

Review 8.  The Next Era: Deep Learning in Pharmaceutical Research.

Authors:  Sean Ekins
Journal:  Pharm Res       Date:  2016-09-06       Impact factor: 4.200

9.  Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Authors:  Thomas Lane; Daniel P Russo; Kimberley M Zorn; Alex M Clark; Alexandru Korotcov; Valery Tkachenko; Robert C Reynolds; Alexander L Perryman; Joel S Freundlich; Sean Ekins
Journal:  Mol Pharm       Date:  2018-04-26       Impact factor: 4.939

10.  Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Authors:  Alexandru Korotcov; Valery Tkachenko; Daniel P Russo; Sean Ekins
Journal:  Mol Pharm       Date:  2017-11-13       Impact factor: 4.939

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