Molecular Design

Molecular design leverages computational methods to create molecules with desired properties, accelerating drug discovery and materials science. Current research heavily employs machine learning, particularly generative models like variational autoencoders (VAEs), transformers, and GFlowNets, often integrated with reinforcement learning and Bayesian optimization to navigate the vast chemical space and ensure synthetic feasibility. These advancements enable efficient exploration of synthesizable molecules, optimization of multiple properties simultaneously, and the incorporation of diverse data types, including text descriptions and high-content imaging data, to guide the design process. The resulting improvements in efficiency and accuracy have significant implications for various scientific fields and industrial applications.