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Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Official Github for "PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling" (NeurIPS 2023 Workshop)
Learning Structure-based Pocket Representations for Protein-Ligand Interaction Prediction
Code for "Multilevel Graph Matching Networks for Deep Graph Similarity Learning"
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery
Official implementation of NeurIPS'21 paper"Motif-based Graph Self-Supervised Learning for Molecular Property Prediction"
The official codes and implementations of HimGNN model in paper:"HimGNN:a novel hierarchical molecular representations learning framework for property prediction"
Mixed continous/categorical flow-matching model for de novo molecule generation.
Deep Learning-based Bioisosteric Replacements for Optimization of Multiple Molecular Properties
Notes solutions and code for Jackson Electrodynamics 3rd Edition
Modelling protein conformational landscape with Alphafold
An object-aware diffusion model for generating chemical reactions
Implementation for ICML 2024 paper "MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space"
Neural Network Force Field based on PyTorch
This is the first model that can simultaneously predict the RMSD of the ligand docking pose and the binding strength against the target.
Implementation of Geometrically Equivariant Graph Neural Networks (GNNs) in PyTorch.
Improved antibody structure-based design using inverse folding
Implementation of Alphafold 3 in Pytorch
FABind: Fast and Accurate Protein-Ligand Binding (NeurIPS 2023)
A convinent tool to visualize AlphaFold 3 prediction results with PyMOL and matplotlib
Implementation of Alpha Fold 3 from the paper: "Accurate structure prediction of biomolecular interactions with AlphaFold3" in PyTorch