RefineGNN - Iterative refinement graph neural network for antibody sequence-structure co-design (RefineGNN)

Last update: Dec 31, 2022

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Deep Learning RefineGNN

Overview

Iterative refinement graph neural network for antibody sequence-structure co-design (RefineGNN)

This is the implementation of our ICLR 2022 paper: https://arxiv.org/pdf/2110.04624.pdf

Warning: this repo is still under construction...

Language model and CDR structure prediction (Section 4.1)

Antibody structure data is retreived from the Structural Antibody Database (SAbDab). The training, validation, and test sets are provided in data/sabdab. Please decompress the files in that folder. To train a generative model for CDR-H3, please run

python ab_train.py --cdr_type 3

Antigen-binding antibody design (Section 4.2)

antibody-antigen binding data is provided in data/rabd. To train a generative model, please run

python ab_train.py --train_path data/rabd/train.jsonl --val_path data/rabd/val.jsonl --test_path data/rabd/test.jsonl

Owner

Wengong Jin

GitHub Repository

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RefineGNN - Iterative refinement graph neural network for antibody sequence-structure co-design (RefineGNN)

Related tags

Overview

Iterative refinement graph neural network for antibody sequence-structure co-design (RefineGNN)

Language model and CDR structure prediction (Section 4.1)

Antigen-binding antibody design (Section 4.2)

Owner

Wengong Jin

Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

A real-time speech emotion recognition application using Scikit-learn and gradio

code for "Self-supervised edge features for improved Graph Neural Network training",

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