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Search results for graph neural networks drug discovery

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15 search results found

Torchdrug ⭐ 1,297

A powerful and flexible machine learning platform for drug discovery

Graphein ⭐ 932

Protein Graph Library

Chemicalx ⭐ 657

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

Dgl Lifesci ⭐ 558

Python package for graph neural networks in chemistry and biology

Equibind ⭐ 321

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

Equidock_public ⭐ 81

EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

Source code for "Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures"

This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

Official implementation of DrugGEN

TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design

(differentiable) gradient-based optimization on a chemical graph for de novo molecule design/optimization (ICLR 2022)

Official Github for "Molecular generative model via retrosynthetically prepared chemical building block assembly" (Advanced Science)

Canceromicsnet ⭐ 10

A Graph Neural Network Model for prediction of the effectiveness of a drug on a given cancer cell lines

Pocket2drug ⭐ 9

Pytorch implementation of Pocket2Drug: a generative deep learning model to predict binding drugs for ligand-binding sites.

Awesome Gnn Based Drug Discovery ⭐ 8

This is a curated list of resources and tools related to using Graph Neural Networks (GNNs) for drug discovery.

Biomedgps ⭐ 7

A knowledge graph system with graph neural network for drug repurposing and disease mechanism.

Pocket Cfdm ⭐ 6

Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets

MolKGNN is a deep learning model for predicting biological activity or molecular properties. It features in 1. SE(3)-invariance 2. conformation-invariance 3. interpretability. MolKGNN uses a novel molecular convolution to leverage the similarity of molecular neighborhood and kernels. It shows superior results in realistic drug discovery datasets.

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