GraphCPA

This repository has our code for our model GraphCPA and its variants, created for CPSC 483.

Usage

• Download checkpoint of pretrained molecule encoder from here and place into jeffrey/base_checkpoint.
• Download de_train.parquet from here and place into the root of the repository.
• For all training and inference besides from inside the jeffrey (molecule transformer), create and activate a Conda environment from environment.yml. For the variant of the model with the molecule transformer in jeffrey, use transformer_environment.yml.

GraphCPA Base

• Use finetune.py and inference.py.

GraphCPA + Molecule Transformer

• Use jeffrey/finetune.py and inference.py for the trainable molecule encoder.
• Use jeffrey/finetune_fixed.py and inference_fixed.py for the fixed molecule encoder.

GraphCPA + Gene Relationship GCN

• Use andrew/finetune.py and andrew/inference.py.

GraphCPA + Gene Relationship GCN + Chromatin Attention

• Use bill/finetune.py and bill/inference.py.

Evaluation

• Upload the csv files from inference to this Kaggle competition for results on the private test set.