Advances in generative biology and bio-inspired artificial intelligence

作者：Wen-Ye ZENG, Sen-Yu ZHENG, Xiao-Qin ZHAO, Zhu WANG, Yong-Juan ZHANG, Zheng-Yuan MA · 发表于：Shengming kexue · 年份：2026 · DOI：10.3724/cbls.2026038 · 研究领域：Machine Learning in Bioinformatics、Single-cell and spatial transcriptomics、Advanced Technologies and Applied Computing

The convergence of artificial intelligence (AI) and life sciences has evolved from unidirectional tool-assisted research into a bidirectional, co-evolutionary paradigm characterized by mutual knowledge transfer and synergistic advancement. This review aims to systematically examine the latest progress in this interdisciplinary fusion, delineate the underlying logic of their interactive empowerment, and discuss the transformative potential and remaining challenges of this symbiotic relationship for future scientific research. The foundational logic of AI-life science integration rests on three pillars: the analogy between biological and computational information-processing paradigms, a shared mathematical language for modeling systemic complexity, and a spiraling cycle of bidirectional knowledge transfer from “AI understanding life” to “life inspiring AI”. Building on this foundation, the review first surveys the innovative development of Generative Biology across multiple scales. At the molecular level, breakthroughs such as AlphaFold-series models and diffusion-based protein design frameworks (e.g., RFdiffusion) have fundamentally shifted the paradigm from observation to de novo creation, enabling atomic-precision design of proteins, enzymes, and drug candidates. At the cellular level, single-cell foundation models and virtual cell frameworks leverage deep generative architectures to learn transferable cell-state representations, supporting perturbation response prediction...