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
Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
Víctor Sabanza-Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch
Scalable Multi-Task Transfer Learning for Molecular Property Prediction
Chanhui Lee, Dae-Woong Jeong, Sung Moon Ko, Sumin Lee, Hyunseung Kim, Soorin Yim, Sehui Han, Sungwoong Kim, Sungbin Lim