DL-PBP-Design

Deep learning and optimization code to generate plastic binding peptides (PBPs)

Overview

• camsol_calculation/ contains a script to compute the CamSol value for a given peptide
• data/ contains PepBD datasets for PE- and PS-binding that are used to train the deep learning models as well as a small sample dataset with 500 peptides designed by MCTS and their predicted PepBD scores for demonstration purpose
• examples/ contains jupyter notebooks to demonstrate how to run our model to generate peptides and how to perform SHAP analysis based on the trained score predictors
• peptide_generators/ contains the MCTS generators described in the paper
• score_predictors/ contains both the trained LSTM models for PE and PS binding prediction

Package Requirements

• python 3.8.2
• pandas 1.4.1
• shap 0.41.0
• scikit-learn 1.2.2
• numpy 1.20.1
• tensorflow 2.6.0

Citation

TBD