Recent advances in generative biology for biotherapeutic discovery

• Published in: Trends in pharmacological sciences (Regular ed.) Vol. 45; no. 3; pp. 255 - 267
• Main Authors: Mock, Marissa, Langmead, Christopher James, Grandsard, Peter, Edavettal, Suzanne, Russell, Alan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2024
• Subjects: Artificial Intelligence; biologics; Biology; drug discovery; Drug Discovery - methods; generative biology; Humans; machine learning; multispecifics; protein engineering; multispecifics; protein engineering; biologics; drug discovery; machine learning; generative biology
• ISSN: 0165-6147; 1873-3735
• DOI: 10.1016/j.tips.2024.01.003

Protein therapeutics are an important and growing class of medicine with discovery and development cycles that are long, expensive, and frequently unsuccessful.The current wave of drug development is dominated by multispecific drugs, which can achieve impressive therapeutic efficacy by interacting with more than one target to accomplish complex biological function.Generative biology, the integration of modern artificial intelligence (AI)/machine learning (ML) methods with advanced life science technologies, will accelerate the discovery and development of complex protein therapeutics.Effectively translating the potential of these innovations into practically impactful advances in protein drug development will require seamless integration of both wet- and dry-laboratory technologies. 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. 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.