  3D generative models have shown significant promise in structure-based drug
design (SBDD), particularly in discovering ligands tailored to specific target
binding sites. Existing algorithms often focus primarily on ligand-target
binding, characterized by binding affinity. Moreover, models trained solely on
target-ligand distribution may fall short in addressing the broader objectives
of drug discovery, such as the development of novel ligands with desired
properties like drug-likeness, and synthesizability, underscoring the
multifaceted nature of the drug design process. To overcome these challenges,
we decouple the problem into molecular generation and property prediction. The
latter synergistically guides the diffusion sampling process, facilitating
guided diffusion and resulting in the creation of meaningful molecules with the
desired properties. We call this guided molecular generation process as TAGMol.
Through experiments on benchmark datasets, TAGMol demonstrates superior
performance compared to state-of-the-art baselines, achieving a 22% improvement
in average Vina Score and yielding favorable outcomes in essential auxiliary
properties. This establishes TAGMol as a comprehensive framework for drug
generation.
