Molecular Generative

Molecular generative models aim to design novel molecules with desired properties, accelerating drug discovery and materials science. Current research emphasizes improving the quality and efficiency of molecule generation, focusing on architectures like generative adversarial networks (GANs), diffusion models (including equivariant versions), and flow-based models, often incorporating reinforcement learning and advanced tokenization techniques for improved control over molecular properties and structure. These advancements are significantly impacting drug design by enabling the efficient exploration of chemical space and the generation of molecules with optimized characteristics, such as high binding affinity and synthetic accessibility.