Literature DB >> 33501158

Exploring Novel Biologically-Relevant Chemical Space Through Artificial Intelligence: The NCATS ASPIRE Program.

Katharine K Duncan1, Dobrila D Rudnicki1, Christopher P Austin1, Danilo A Tagle1.   

Abstract

In recent years, artificial intelligence (AI)/machine learning (ML; a subset of AI) have become increasingly important to the biomedical research community. These technologies, coupled to big data and cheminformatics, have tremendous potential to improve the design of novel therapeutics and to provide safe and effective drugs to patients. A National Center for Advancing Translational Sciences (NCATS) program called A Specialized Platform for Innovative Research Exploration (ASPIRE) leverages advances in AI/ML, automated synthetic chemistry, and high-throughput biology, and seeks to enable translation and drug development by catalyzing exploration of biologically active chemical space. Here we discuss the opportunities and challenges surrounding the application of AI/ML to the exploration of novel biologically relevant chemical space as part of ASPIRE.
Copyright © 2020 Duncan, Rudnicki, Austin and Tagle.

Entities:  

Keywords:  artificial intelligence; biomedical research; cheminformatics; drug discovery; machine learning; pharmaceutical development; translational science

Year:  2020        PMID: 33501158      PMCID: PMC7805902          DOI: 10.3389/frobt.2019.00143

Source DB:  PubMed          Journal:  Front Robot AI        ISSN: 2296-9144


  28 in total

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Review 4.  Computational prediction of chemical reactions: current status and outlook.

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Journal:  Drug Discov Today       Date:  2018-03-03       Impact factor: 7.851

Review 5.  Automating drug discovery.

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

Review 6.  Machine learning in chemoinformatics and drug discovery.

Authors:  Yu-Chen Lo; Stefano E Rensi; Wen Torng; Russ B Altman
Journal:  Drug Discov Today       Date:  2018-05-08       Impact factor: 7.851

7.  Mapping biologically active chemical space to accelerate drug discovery.

Authors:  G Sitta Sittampalam; Dobrila D Rudnicki; Danilo A Tagle; Anton Simeonov; Christopher P Austin
Journal:  Nat Rev Drug Discov       Date:  2019-02       Impact factor: 84.694

8.  ChEMBL: a large-scale bioactivity database for drug discovery.

Authors:  Anna Gaulton; Louisa J Bellis; A Patricia Bento; Jon Chambers; Mark Davies; Anne Hersey; Yvonne Light; Shaun McGlinchey; David Michalovich; Bissan Al-Lazikani; John P Overington
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9.  MoleculeNet: a benchmark for molecular machine learning.

Authors:  Zhenqin Wu; Bharath Ramsundar; Evan N Feinberg; Joseph Gomes; Caleb Geniesse; Aneesh S Pappu; Karl Leswing; Vijay Pande
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Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

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  1 in total

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Authors:  Mazarine Laurent; Stéphane Bostyn; Mathieu Marchivie; Yves Robin; Sylvain Routier; Frédéric Buron
Journal:  RSC Adv       Date:  2021-05-28       Impact factor: 4.036

  1 in total

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