Molecule Optimization

Molecule optimization aims to efficiently discover molecules with desired properties, a crucial task in drug discovery and materials science. Current research heavily focuses on leveraging machine learning, particularly large language models and diffusion models, to generate and optimize molecules within latent spaces, often employing techniques like Bayesian optimization and evolutionary algorithms to navigate complex chemical landscapes. These advancements promise to accelerate the identification of novel molecules with improved properties, significantly impacting drug development and materials design by reducing the time and cost associated with traditional experimental approaches. The development of unified frameworks integrating various stages of the molecule discovery pipeline is also a key area of focus.