  Advancements in deep generative modeling have changed the paradigm of drug
discovery. Among such approaches, target-aware methods that exploit 3D
structures of protein pockets were spotlighted for generating ligand molecules
with their plausible binding modes. While docking scores superficially assess
the quality of generated ligands, closer inspection of the binding structures
reveals the inconsistency in local interactions between a pocket and generated
ligands. Here, we address the issue by explicitly generating non-covalent
interactions (NCIs), which are universal patterns throughout protein-ligand
complexes. Our proposed model, NCIDiff, simultaneously denoises NCI types of
protein-ligand edges along with a 3D graph of a ligand molecule during the
sampling. With the NCI-generating strategy, our model generates ligands with
more reliable NCIs, especially outperforming the baseline diffusion-based
models. We further adopted inpainting techniques on NCIs to further improve the
quality of the generated molecules. Finally, we showcase the applicability of
NCIDiff on drug design tasks for real-world settings with specialized
objectives by guiding the generation process with desired NCI patterns.
