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.

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生物治疗发现的生成生物学的最新进展

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，彻底改变了设计具有规定特性的新型生物分子的过程，使药物发现者能够在设计下一代蛋白质疗法时摆脱生物学的限制。仍然存在重大障碍，即：（i）药物发现固有的复杂性，（ii）过去几年中出现的令人眼花缭乱的有前途的计算和实验技术，以及（iii）相关蛋白质序列的可用性有限 -类药物分子的功能数据。需要关注对蛋白质药物发现最实际有效的计算方法，以及构建实验平台以生成最适合这些方法的数据。在这里，我们讨论计算和实验生命科学的最新进展，这些进展对于影响蛋白质药物发现的速度和成功至关重要。