Novel Molecule

Research on novel molecule generation aims to design molecules with specific properties for applications in drug discovery and materials science. Current efforts focus on developing generative models, leveraging architectures like variational autoencoders (VAEs), graph neural networks (GNNs), transformers, and reinforcement learning algorithms, often incorporating large language models (LLMs) to integrate textual domain knowledge. These advancements improve the efficiency and accuracy of molecule design by addressing challenges like synthetic accessibility and the generation of molecules with diverse and desirable properties. The resulting impact is accelerated discovery and optimization of molecules for various applications, including drug development and materials engineering.