README.md

AbMEGD Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding

Install

Environment

conda env create -f env.yaml -n AbMEGD
conda activate AbMEGD

The default cudatoolkit version is 11.3. You may change it in env.yaml.

Datasets and Trained Weight

Protein structures in the SAbDab dataset can be downloaded here. Extract all_structures.zip into the data folder.

The data folder contains a snapshot of the dataset index (sabdab_summary_all.tsv). You may replace the index with the latest version here.

Design and Optimize Antibodies

3 design modes are available. Each mode corresponds to a config file in the configs/test folder:

Config File	Description
codesign_single.yml	Sample both the sequence and structure of one CDR.
codesign_multicdrs.yml	Sample both the sequence and structure of all the CDRs simultaneously.
abopt_singlecdr.yml	Optimize the sequence and structure of one CDR.

Train

python train.py ./configs/train/<config-file-name>