Molecular Generation

Molecular generation uses artificial intelligence to design novel molecules with desired properties, primarily for drug discovery and materials science. Current research focuses on improving the quality and efficiency of generation, employing various architectures like generative adversarial networks (GANs), diffusion models, and generative flow networks (GFlowNets), often incorporating techniques like reinforcement learning and training-free guidance. These advancements enable the exploration of vast chemical spaces, accelerating the identification of molecules with optimized properties for specific applications, thereby significantly impacting drug design and materials development.