Integrated AI-Driven Multi-Objective Optimization of Sulfamoylbenzamide-Based HBV Capsid Assembly Modulators: Discovery of 11B-6 with Superior Potency and Drug-like Properties

Abstract Current capsid assembly modulators (CAMs) require improvements in antiviral potency and drug-like properties. Herein, we report a multi-objective optimization workflow for the NVR 3-778 by integrating structure-based fragment growing with systematic medicinal chemistry optimization, activity prediction, and multitask prediction of drug-like properties. This led to 48 novel analogs. Among them, 11B-6 showed superior antiviral activity in HepAD38 cells (EC50 = 0.04 ± 0.02 μM), outperforming NVR 3-778 (EC50 = 0.50 ± 0.17 μM). In HBV-infected HepG2-NTCP cells, 11B-6 inhibited secreted HBV DNA with an EC50 of 3.71 nM, about 134-fold more potent than NVR 3-778 (EC50 = 497.9 nM). Structural biology analyses revealed a distinct, more stable binding mode of 11B-6 at the capsid dimer−dimer interface. 11B-6 also showed lower plasma protein binding than NVR 3-778 (84.3% vs. 100%). These results highlight 11B-6 as a promising next-generation HBV CAM, representing a valuable starting point for advancing its development.