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
Reinforcement Learning-Driven Linker Design via Fast Attention-based Point Cloud Alignment
Rebecca M. Neeser, Mehmet Akdel, Daniel Kovtun, Luca Naef
Automated 3D Pre-Training for Molecular Property Prediction
Xu Wang, Huan Zhao, Weiwei Tu, Quanming Yao
Von Mises Mixture Distributions for Molecular Conformation Generation
Kirk Swanson, Jake Williams, Eric Jonas