Xiaoying Dong

Abstract 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.

1 Introduction Lung cancer is the malignant tumor with the highest global incidence and mortality, of which non-small cell lung cancer (NSCLC) constitutes approximately 85% of cases [1, 2]. The incidence of EGFR mutation (most commonly found in exons 18–21) in patients with NSCLC is approximately 51% [3]. Tumors with EGFR mutations are highly sensitive to EGFR tyrosine kinase inhibitors (EGFR TKIs), including afatinib, erlotinib, gefitinib, and osimertinib.