Known Molecule
Research on known molecules focuses on developing efficient methods for generating, optimizing, and predicting their properties, primarily to accelerate drug discovery and materials science. Current efforts leverage machine learning, employing architectures like graph neural networks, transformers, and diffusion models, often incorporating 3D structural information and multi-fidelity approaches to improve accuracy and efficiency. These advancements enable more rapid exploration of chemical space, leading to improved predictions of molecular properties and the design of molecules with desired characteristics, ultimately impacting drug development and materials design.
Papers
MolBind: Multimodal Alignment of Language, Molecules, and Proteins
Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. Honavar
Representing Molecules as Random Walks Over Interpretable Grammars
Michael Sun, Minghao Guo, Weize Yuan, Veronika Thost, Crystal Elaine Owens, Aristotle Franklin Grosz, Sharvaa Selvan, Katelyn Zhou, Hassan Mohiuddin, Benjamin J Pedretti, Zachary P Smith, Jie Chen, Wojciech Matusik