Literature DB >> 33176630

New Perspectives of Machine Learning in Drug Discovery.

Simona Musella1, Giulio Verna1, Alessio Fasano1, Simone Di Micco1.   

Abstract

Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to clinical trial. This approach is harnessing the impact of computer-aided drug discovery thanks to large available data sets for drug candidates and its new and complex manner of information interpretation to identify patterns for the study scope. In the present review, recent applications related to drug discovery and therapies are assessed, and limitations and future perspectives are analyzed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Keywords:  ADMET; Machine learning; artificial intelligence; drug design.; drug repurposing; personalized medicine; synthesis planning; virtual screening

Year:  2020        PMID: 33176630     DOI: 10.2174/0929867327666201111144048

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  3 in total

Review 1.  Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: lessons from the pandemic and preparing for future health crises.

Authors:  Natesh Singh; Bruno O Villoutreix
Journal:  Comput Struct Biotechnol J       Date:  2021-04-26       Impact factor: 7.271

Review 2.  Deep learning tools for advancing drug discovery and development.

Authors:  Sagorika Nag; Anurag T K Baidya; Abhimanyu Mandal; Alen T Mathew; Bhanuranjan Das; Bharti Devi; Rajnish Kumar
Journal:  3 Biotech       Date:  2022-04-09       Impact factor: 2.893

3.  Machine learning approaches to optimize small-molecule inhibitors for RNA targeting.

Authors:  Hadar Grimberg; Vinay S Tiwari; Benjamin Tam; Lihi Gur-Arie; Daniela Gingold; Lea Polachek; Barak Akabayov
Journal:  J Cheminform       Date:  2022-02-02       Impact factor: 5.514

  3 in total

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