A Comparison of Small Molecule Generation Methods in Structure-Based Drug Design: Quantum-Aided Drug Design (QuADD) vs Bond and Interaction generating Diffusion model (BInD)

Quantum-Aided Drug Design (QuADD) is a platform that utilizes quantum computing to formulate molecular design as a multi-objective optimization problem, enabling the generation of drug-like molecules optimized for interactions within a defined binding pocket. Generative artificial intelligence has similarly emerged as a strategy for exploring chemical space and designing novel molecular structures. The Bond and Interaction generating Diffusion model (BInD), an AI-based application, applies reverse diffusion techniques to produce structurally diverse candidate molecules. In this work, we present a controlled comparison between these two structure-based molecular generation systems. Both approaches produced novel molecules compatible with the evaluated binding site. While BInD generated candidates exhibiting greater structural diversity, QuADD-generated molecules more consistently satisfied prioritization criteria related to predicted binding affinity, drug-likeness, and preservation of key protein–ligand interactions. These findings suggest that constraint-driven optimization can provide advantages in generating synthetically plausible, pocket-aware candidate molecules suitable for early-stage lead discovery, while probabilistic generative strategies may promote broader structural exploration.