Generative Molecular Design

Generative molecular design uses artificial intelligence to create novel molecules with desired properties, accelerating drug discovery and materials science. Current research focuses on improving the efficiency and accuracy of generative models, employing architectures like variational autoencoders and large language models, often enhanced by techniques such as reinforcement learning and active learning to optimize for multiple properties simultaneously. These advancements aim to overcome limitations in sample efficiency and improve the correlation between in silico predictions and experimental results, ultimately leading to faster and more effective design of molecules for various applications.