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

Last update: Dec 31, 2022

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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

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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

Training vision models with full-batch gradient descent and regularization

Official implementation of "Watermarking Images in Self-Supervised Latent-Spaces"

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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

Training vision models with full-batch gradient descent and regularization

Official implementation of "Watermarking Images in Self-Supervised Latent-Spaces"

A simple consistency training framework for semi-supervised image semantic segmentation

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation).

PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

A basic neural network for image segmentation.

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

Joint Learning of 3D Shape Retrieval and Deformation, CVPR 2021

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

✔️ Visual, reactive testing library for Julia. Time machine included.

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Use unsupervised and supervised learning to predict stocks

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A clear, concise, simple yet powerful and efficient API for deep learning.

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