Single‐Sequence Deep Learning Delivers Crystal‐Quality Models of Covalent K‐Ras G12 Hotspot Complexes

ABSTRACT Structure‐based design of covalent drugs has achieved tremendous success by understanding and leveraging the three‐dimensional interactions between small‐molecule drug candidates and their protein targets. However, this approach traditionally relies on high‐resolution co‐complex structures obtained by X‐ray crystallography, NMR, or cryo‐EM, methods that are time‐consuming and resource‐intensive. Here we show that Chai‐1, a publicly available structure prediction tool that accepts user‐defined ligands, is able to accurately predict covalent K‐Ras(G12C) complexes without using a multiple sequence alignment (MSA). Chai‐1 yields pocket‐aligned RMSDs < 2 Å for chemically diverse K‐Ras(G12C) inhibitors, ranging from ARS‐853 to BBO‐8520. In addition to the conventional acrylamide‐based covalent K‐Ras(G12C) inhibitors, Chai‐1 with a covalent‐bond restraint successfully reproduced the binding poses of covalent K‐Ras(G12D) and K‐Ras(G12S) inhibitors, while showing limitations in capturing chemical details such as accounting for leaving groups, bond properties, and stereochemistry. Chai‐1 also provides ~40‐fold higher throughput than state‐of‐the‐art AlphaFold3 while maintaining comparable pose accuracy. Together, these findings establish Chai‐1 as an accessible and computationally efficient tool for covalent protein‐ligand co‐complex structure prediction, with its covalent‐restraint mode offering a unique solution to accelerate covalent drug discovery, especially for challenging targets beyond cysteine.