Molecular Conformation
Molecular conformation, the three-dimensional arrangement of atoms in a molecule, is crucial for understanding its properties and functions. Current research focuses on developing accurate and efficient methods for generating and analyzing molecular conformations, employing diverse approaches such as diffusion models, graph neural networks, and flow-based generative models, often incorporating physical constraints and symmetries (e.g., SE(3) invariance). These advancements are significantly impacting fields like drug discovery and materials science by enabling faster and more accurate virtual screening, protein design, and reaction pathway prediction. The development of robust benchmarks and datasets is also a key area of focus, facilitating the comparison and improvement of different methods.
Papers
Role of Structural and Conformational Diversity for Machine Learning Potentials
Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding
Inverse folding for antibody sequence design using deep learning
Frédéric A. Dreyer, Daniel Cutting, Constantin Schneider, Henry Kenlay, Charlotte M. Deane