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Lindley S, Lu Y, Shukla D. The Experimentalist's Guide to Machine Learning for Small Molecule Design. ACS Appl Bio Mater 2024; 7:657-684. [PMID: 37535819 PMCID: PMC10880109 DOI: 10.1021/acsabm.3c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023]
Abstract
Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.
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Affiliation(s)
- Sarah
E. Lindley
- Department
of Bioengineering, University of Illinois, Urbana−Champaign, Illinois 61801, United States
| | - Yiyang Lu
- Department
of Chemical and Biomolecular Engineering, University of Illinois, Urbana−Champaign, Illinois 61801, United States
| | - Diwakar Shukla
- Department
of Bioengineering, University of Illinois, Urbana−Champaign, Illinois 61801, United States
- Department
of Chemical and Biomolecular Engineering, University of Illinois, Urbana−Champaign, Illinois 61801, United States
- Center
for Biophysics & Computational Biology, University of Illinois, Urbana−Champaign, Illinois 61801, United States
- Department
of Plant Biology, University of Illinois, Urbana−Champaign, Illinois 61801, United States
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Boniolo F, Dorigatti E, Ohnmacht AJ, Saur D, Schubert B, Menden MP. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin Drug Discov 2021; 16:991-1007. [PMID: 34075855 DOI: 10.1080/17460441.2021.1918096] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.
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Affiliation(s)
- Fabio Boniolo
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Emilio Dorigatti
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Statistical Learning and Data Science, Department of Statistics, Ludwig Maximilian Universität München, Munich, Germany
| | - Alexander J Ohnmacht
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany
| | - Dieter Saur
- School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Benjamin Schubert
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany.,German Centre for Diabetes Research (DZD e.V.), Neuherberg, Germany
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