Engineering new molecules with desirable functions and properties has the
potential to extend our ability to engineer proteins beyond what nature has so
far evolved. Advances in the so-called "de novo" design problem have recently
been brought forward by developments in artificial intelligence. Generative
architectures, such as language models and diffusion processes, seem adept at
generating novel, yet realistic proteins that display desirable properties and
perform specified functions. State-of-the-art design protocols now achieve
experimental success rates nearing 20%, thus widening the access to de novo
designed proteins. Despite extensive progress, there are clear field-wide
challenges, for example in determining the best in silico metrics to prioritise
designs for experimental testing, and in designing proteins that can undergo
large conformational changes or be regulated by post-translational
modifications and other cellular processes. With an increase in the number of
models being developed, this review provides a framework to understand how
these tools fit into the overall process of de novo protein design. Throughout,
we highlight the power of incorporating biochemical knowledge to improve
performance and interpretability.

利用人工智能等方法进行蛋白质工程和全新设计的研究方案，如生成模型和扩散过程，能够生成展现出所需特性和功能的全新而真实的蛋白质，在优先测试设计方案方面仍然面临一些挑战，因此需要综合生物化学知识提高性能和可解释性。