Replicating the results of "Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery"

[DimitrisAlivas]

In this work, there is a lot of experimentation around KGE models for drug discovery.

Link to paper: paper

They are using the same BioKG as we do, which means that we should be able to replicate their results as long as we follow the same setup.

The script for the specific experiment is below.

#!/bin/bash
#SBATCH --job-name=bioblp-complex
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=6
#SBATCH --ntasks-per-node=1
#SBATCH --time=50:00:00
#SBATCH --mem=10G
#SBATCH --partition=gpu_shared
#SBATCH --gres=gpu:1

PROJ_FOLDER=BioBLP
OUT_FOLDER=models

# Copy data to scratch
cp -r $HOME/Projects/$PROJ_FOLDER $TMPDIR
cd $TMPDIR/$PROJ_FOLDER

source .venv/bin/activate

python -m bioblp.train \
--train_triples=data/ootb_biokg_triples/train_triples.csv \
--valid_triples=data/ootb_biokg_triples/val_triples.csv \
--test_triples=data/ootb_biokg_triples/test_triples.csv \
--dimension=464 \
--learning_rate=0.09 \
--num_epochs=600 \
--batch_size=128 \
--num_negatives=91 \
--log_wandb=True \
--loss_fn='NSSALoss' \
--notes="Replicating hyper-param experiment from Charlie paper"

# Keep files generated during job
RESULTS_FOLDER=$HOME/Projects/$PROJ_FOLDER-$OUT_FOLDER
mkdir -p $RESULTS_FOLDER
cp -r $TMPDIR/$PROJ_FOLDER/$OUT_FOLDER/* $RESULTS_FOLDER

I am currently running this experiment using the ootb-biokg-triples (out-of-the-box).

The purpose of this experiment is to pinpoint whether the removal of benchmark triples had such a huge effect in our performance, or whether there is an unidentified mistake on our side.

[DimitrisAlivas]

Closing this since it's been done for a while.