Michael Heinzinger1 and Burkhard Rost1,2,3,4 1Technical University of Munich (TUM) School of School of Computation, Information and Technology (CIT), Bioinformatics and Computational Biology - i12, 85748 Garching/Munich, Germany 2Institute for Advanced Study (TUM-IAS), 85748 Garching/Munich, Germany 3TUM School of Life Sciences Weihenstephan (WZW), 85354 Freising, Germany 4Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA Correspondence: mheinzingerrostlab.org

From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.