دورية أكاديمية
أعرض في EDS

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

التفاصيل البيبلوغرافية
العنوان: Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.
المؤلفون: Akbar R; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway., Bashour H; School of Life Sciences, University of Warwick, Coventry, UK., Rawat P; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.; Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India., Robert PA; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway., Smorodina E; Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia., Cotet TS; Department of Life Sciences, Imperial College London, UK., Flem-Karlsen K; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.; Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway., Frank R; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway., Mehta BB; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway., Vu MH; Department of Linguistics and Scandinavian Studies, University of Oslo, Norway., Zengin T; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.; Department of Bioinformatics, Mugla Sitki Kocman University, Turkey., Gutierrez-Marcos J; School of Life Sciences, University of Warwick, Coventry, UK., Lund-Johansen F; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway., Andersen JT; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.; Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway., Greiff V; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
المصدر: MAbs [MAbs] 2022 Jan-Dec; Vol. 14 (1), pp. 2008790.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't; Review
اللغة: English
بيانات الدورية: Publisher: Taylor & Francis Country of Publication: United States NLM ID: 101479829 Publication Model: Print Cited Medium: Internet ISSN: 1942-0870 (Electronic) Linking ISSN: 19420862 NLM ISO Abbreviation: MAbs Subsets: MEDLINE
أسماء مطبوعة: Publication: 2015- : Philadelphia, PA : Taylor & Francis
Original Publication: Austin, TX : Landes Bioscience
مواضيع طبية MeSH: Antineoplastic Agents, Immunological* , Artificial Intelligence*, Algorithms ; Antibodies, Monoclonal/therapeutic use ; Machine Learning
مستخلص: Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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فهرسة مساهمة: Keywords: Machine learning; antibody; antigen; artificial intelligence; developability; drug design
المشرفين على المادة: 0 (Antibodies, Monoclonal)
0 (Antineoplastic Agents, Immunological)
تواريخ الأحداث: Date Created: 20220316 Date Completed: 20220407 Latest Revision: 20231102
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC8928824
DOI: 10.1080/19420862.2021.2008790
PMID: 35293269
قاعدة البيانات: MEDLINE
الوصف
تدمد:1942-0870
DOI:10.1080/19420862.2021.2008790