AI4Aggregation

This repo contains code accompanying the publication Amino Acid Composition drives Peptide Aggregation: Predicting Aggregation for Improved Synthesis, including scripts to reproduce all results.

Installation

This project utilises poetry as package manager. Install the package run:

poetry install

Preprocessing data

To create the dataset used to train models, use the following script:

poetry run create_combined_data --uzh_dataset_path <Path to UZH dataset> --mit_dataset_path <Path to MIT dataset> --save_path data/combined_data.csv

For this both the UZH and MIT dataset are required. For the UZH dataset download the file uzh_data_clean.csv from the Zenodo record corresponding to this publication. The MIT dataset can be found in the corresponding GitHub repo here.

Reproducing Training results

We provide a set of scripts to replicate the results obtained in the paper. All scripts require a path to where the results of the experiments are saved. The HuggingFace models require GPUs to train:

bash scripts/run_hf_models.sh <Path to Experiment Folder>
bash scripts/run_sklearn_models.sh <Path to Experiment Folder>
bash scripts/run_sklearn_shuffled.sh <Path to Experiment Folder>
bash scripts/run_wof_sklearn.sh <Path to Experiment Folder>

Explainability

To explain the predictions of the models we use Shap values. To reproduce our results use the following scripts:

poetry run explain_model --data_path data/combined_data.csv --output_path <Path where results should be stored>