3D Molecule Generation

3D molecule generation uses machine learning to design novel molecules with desired properties, primarily for drug discovery. Current research heavily employs diffusion models, often enhanced with equivariant architectures or optimal transport methods, to generate 3D molecular structures efficiently and accurately, addressing challenges like slow sampling times and chemical validity. These advancements focus on improving the controllability of generation, incorporating textual descriptions or substructure constraints, and enhancing the realism of generated molecules through explicit modeling of non-covalent interactions. This field significantly impacts drug design by accelerating the discovery of novel drug candidates and enabling structure-based drug design.