Pytorch implementation for paper "ExplainableFold: Understanding AlphaFold Prediction with Explainable AI", KDD 2023.
https://arxiv.org/abs/2301.11765
The ExplainableFold is based on OpenFold's implementation of AlphaFold2. To run these codes, first download the base model https://github.com/aqlaboratory/openfold and setup the environments accordingly.
- To run the experiments, place the explainable_fold folder under the root folder of openfold.
- Download AlphaFold database. Refer to instructions in https://github.com/deepmind/alphafold
- To generate explanations on the test data, take "Most Intolerant Deletion" as example, run:
Remember to set the path to the MSAs database in your machine.
python3 explainable_fold/exp_del.py \ --fasta_dir explainable_fold/xfold_test/\ --loss_type tm \ --opt_type min \ --alp 0.2 \ --leaky 0.1 \ --max_l 384 \ --steps 100 \ --delta_init equal\ --output_dir ./explainable_fold/log \ --model_device "cuda:0" \ --lam 0.00001 \ --lr 0.01 \ --jackhmmer_binary_path lib/conda/envs/openfold_venv/bin/jackhmmer \ --hhblits_binary_path lib/conda/envs/openfold_venv/bin/hhblits \ --hhsearch_binary_path lib/conda/envs/openfold_venv/bin/hhsearch \ --uniref90_database_path .../uniref90/uniref90.fasta \ --mgnify_database_path .../mgnify/mgy_clusters_2018_12.fa \ --bfd_database_path .../bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \ --uniclust30_database_path .../uniclust30/uniclust30_2018_08/uniclust30_2018_08 \ --pdb70_database_path .../pdb70/pdb70 - The generated counterfactual examples can be found in the ./explainable_fold/log folder.
@inproceedings{tan2023explainablefold,
author = {Tan, Juntao and Zhang, Yongfeng},
title = {ExplainableFold: Understanding AlphaFold Prediction with Explainable AI},
booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {2166–2176},
year = {2023}
}