Covalent drugs have long played an essential role in therapeutics, yet computational design approaches remain largely confined to virtual screening of existing libraries. Despite recent advances in deep generative models for drug discovery, methods specifically tailored to de novo covalent drug generation are still lacking. Here we introduce CovaGEN, a conditional latent diffusion framework for the de novo design of covalent inhibitors with enhanced drug-likeness and safety. CovaGEN generates ligands from a drug-like latent space while conditioning on target sequences and employing a classifier to guide the formation of desirable covalent warheads. A reinforcement learning strategy further optimizes the safety profiles of generated molecules. Experimental results demonstrate that CovaGEN effectively generates covalent drugs with the desired covalent warheads, exhibiting strong target protein affinity, favorable drug-likeness, and low toxicity. When applied to EGFR T790M and Mpro, the generated compounds exhibit higher probabilities of covalent binding. Overall, CovaGEN offers a pioneering approach for the de novo design of covalent inhibitors, advancing the discovery of covalent drugs with improved properties.
