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Found 13 papers, 11 papers with code
Transition Path Sampling with Boltzmann Generator-based MCMC Moves
Sampling all possible transition paths between two 3D states of a molecular system has various applications ranging from catalyst design to drug discovery.
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
A significant amount of protein function requires binding small molecules, including enzymatic catalysis.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design.
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Existing GNN benchmarking methods for molecular representation learning focus on comparing the GNNs' performances on some node/graph classification/regression tasks on certain datasets.
Generalized Laplacian Positional Encoding for Graph Representation Learning
Graph neural networks (GNNs) are the primary tool for processing graph-structured data.
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments.
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses.
Ranked #1 on
Blind Docking
on PDBbind
(Top-1 RMSD (Med.) metric)
Graph Anisotropic Diffusion
Traditional Graph Neural Networks (GNNs) rely on message passing, which amounts to permutation-invariant local aggregation of neighbour features.
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery.
Ranked #6 on
Blind Docking
on PDBBind
3D Infomax improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
3D Pre-training improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
Jointly Learnable Data Augmentations for Self-Supervised GNNs
Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs.
