Generalist biological artificial intelligence in modeling the language of life
Generalist biological artificial intelligence in modeling the language of life
https://www.nature.com/articles/s41587-026-03064-w
Publish Date: 2026-03-20 06:56:00
Source Domain: www.nature.com
Crick, F. Central dogma of molecular biology. Nature 227, 561–563 (1970).
Google Scholar
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017).
Naveed, H. et al. A comprehensive overview of large language models. ACM Trans. Intell. Syst. Technol. 16, 106 (2025).
Google Scholar
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M. & Monfardini, G. The graph neural network model. IEEE Trans. Neural Netw. 20, 61–80 (2008).
Google Scholar
Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840–6851 (2020).
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google Scholar
Jiang, K. et al. Rapid in silico directed evolution by a protein language model with EVOLVEpro. Science 387, eadr6006 (2025).
Google Scholar
Brixi, G., Durrant, M.G., Ku, J. et al. Genome modelling and design across all domains of life with Evo 2. Nature https://doi.org/10.1038/s41586-026-10176-5 (2026).
Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).