Marissa Mock, Christopher James Langmead, Peter Grandsard, Suzanne Edavettal, Alan Russell
{"title":"Recent advances in generative biology for biotherapeutic discovery.","authors":"Marissa Mock, Christopher James Langmead, Peter Grandsard, Suzanne Edavettal, Alan Russell","doi":"10.1016/j.tips.2024.01.003","DOIUrl":null,"url":null,"abstract":"<p><p>Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.</p>","PeriodicalId":23250,"journal":{"name":"Trends in pharmacological sciences","volume":null,"pages":null},"PeriodicalIF":13.9000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in pharmacological sciences","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.tips.2024.01.003","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/19 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.
期刊介绍:
Trends in Pharmacological Sciences (TIPS) is a monthly peer-reviewed reviews journal that focuses on a wide range of topics in pharmacology, pharmacy, pharmaceutics, and toxicology. Launched in 1979, TIPS publishes concise articles discussing the latest advancements in pharmacology and therapeutics research.
The journal encourages submissions that align with its core themes while also being open to articles on the biopharma regulatory landscape, science policy and regulation, and bioethics.
Each issue of TIPS provides a platform for experts to share their insights and perspectives on the most exciting developments in the field. Through rigorous peer review, the journal ensures the quality and reliability of published articles.
Authors are invited to contribute articles that contribute to the understanding of pharmacology and its applications in various domains. Whether it's exploring innovative drug therapies or discussing the ethical considerations of pharmaceutical research, TIPS provides a valuable resource for researchers, practitioners, and policymakers in the pharmacological sciences.