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In silico molecule generation enables the rapid creation of an initial pool of drug-like molecules, potentially accelerating significantly drug discovery and design. We explore the use of deep generative models for molecular generation, focusing on recent advancements and challenges. We begin with an introduction to diffusion models, a powerful framework for generating high-quality data through iterative noise processes. Diffusion models on point cloud representations of molecules has been extensively explored, with prevailing methodologies focusing on NN parametrizations that are E(3) invariant. We then examine Field-based Molecule Generation (FMG). Instead of moving points in the 3D space, we generate 3D vector fields, and analyze pros and cons of the two representations. Crucially different than point clouds, we do not constrain our architectures to be rotationally invariant in FMG. We show that keeping rotational invariance in diffusion models must either necessarily disregard molecular chirality or become intractable. We end our discussion with future next steps and ideas on how to integrate molecular generation methods as exploratory models for drug-discovery pipelines.