Mock Marissa, Langmead Christopher James, Grandsard Peter, Edavettal Suzanne, Russell Alan
Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
Trends Pharmacol Sci. 2024 Mar;45(3):255-267. doi: 10.1016/j.tips.2024.01.003. Epub 2024 Feb 19.
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.
生成生物学结合了人工智能(AI)、先进的生命科学技术和自动化技术,以彻底改变设计具有特定性质的新型生物分子的过程,使药物研发人员在设计下一代蛋白质疗法时能够突破生物学的限制。然而,仍然存在重大障碍,即:(i)药物发现本质上的复杂性,(ii)在过去几年中出现的大量令人眼花缭乱的有前景的计算和实验技术,以及(iii)类药物分子相关蛋白质序列-功能数据的有限可用性。有必要专注于对蛋白质药物发现最具实际效果的计算方法,并构建实验平台以生成最适合这些方法的数据。在此,我们讨论计算和实验生命科学领域的最新进展,这些进展对于影响蛋白质药物发现的速度和成功率最为关键。