Compound Library

Compound libraries, vast collections of molecules, are crucial for drug discovery and materials science, serving as the foundation for identifying promising candidates through virtual screening and experimental testing. Current research focuses on improving the efficiency and accuracy of library design and screening using machine learning models, including generative models (e.g., variational autoencoders, GFlowNets), transformer networks, and graph neural networks, often incorporating multimodal data and advanced data splitting techniques to address biases and improve generalizability. These advancements aim to accelerate the identification of novel molecules with desired properties, ultimately impacting drug development, materials design, and other fields requiring efficient exploration of chemical or material space.