Molecule Generation

Molecule generation, the computational design of novel molecules with desired properties, aims to accelerate drug discovery and materials science. Current research heavily utilizes large language models (LLMs), graph neural networks (GNNs), diffusion models, and reinforcement learning (RL) algorithms, often combined in hybrid approaches, to generate molecules based on various conditions, including textual descriptions, target properties, and even 3D protein structures. These advancements improve the efficiency and diversity of molecule generation, addressing challenges like synthesizability and chemical validity. The resulting tools have significant implications for accelerating scientific discovery and enabling the design of new drugs and materials.