Post #867

Generative modeling with proteins (hundreds of them either): EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants Design of Ligand-Binding Proteins with Atomic Flow Matching RapidDock: Unlocking Proteome-scale Molecular Docking Deep Learning for Protein-Ligand Docking: Are We There Yet? ProteinBench: A Holistic Evaluation of Protein Foundation Models Fast and Accurate Blind Flexible Docking Solving Inverse Problems in Protein Space Using Diffusion-Based Priors Crystals and Materials: Flow Matching for Accelerated Simulation of Atomic Transport in Materials MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks Learning the Hamiltonian of Disordered Materials with Equivariant Graph Networks Designing Mechanical Meta-Materials by Learning Equivariant Flows SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models Rethinking the role of frames for SE(3)-invariant crystal structure modeling A Periodic Bayesian Flow for Material Generation ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials Wyckoff Transformer: Generation of Symmetric Crystals PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction