Molecular Optimization

Molecular optimization aims to design molecules with desired properties, a crucial task in drug discovery and materials science. Current research focuses on developing efficient algorithms, including those based on diffusion models, reinforcement learning (often incorporating genetic algorithms or quantum-inspired techniques), and large language models, to navigate the vast chemical space and optimize for multiple properties simultaneously while enhancing explainability and sample efficiency. These advancements are significantly impacting drug design and materials development by accelerating the identification of molecules with improved efficacy, reduced toxicity, and enhanced synthesizability. The field is also actively addressing challenges like bias in evaluation methodologies and improving the controllability of generative models.