Small Molecule
Small molecule research focuses on understanding and manipulating the properties of small organic molecules, primarily for applications in drug discovery and materials science. Current research heavily utilizes machine learning, employing diverse architectures like graph neural networks, transformers, and generative models (e.g., variational autoencoders, GFlowNets) to predict molecular properties, design novel molecules with desired characteristics, and accelerate virtual screening processes. This field is crucial for advancing drug development, enabling the efficient design of therapeutics with improved efficacy and safety profiles, and also holds significant potential for materials science applications.
Papers
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation
Gregory W. Kyro, Anton Morgunov, Rafael I. Brent, Victor S. Batista
From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery
Yuhan Chen, Nuwa Xi, Yanrui Du, Haochun Wang, Jianyu Chen, Sendong Zhao, Bing Qin