Application of Neural Collaborative Filtering & Deep Matrix Factorization on Drug Target Interaction

This is code for the course project for the Collaborative Filtering(CSE640) Fall'18 course offered in IIIT Delhi.

Introduction

We build a recommender system for drug target interactions using two warm start methods: Neural Collaborative Filtering and Deep Matrix Factorization. We apply these methods to the drug target interaction problem to predict what kind of drugs would work on which target site.

Navigating through the code

• drug.py loads the datasets.
• ncf.py, dmf.py, GMF.py, MLP.py contains code for training and saving checkpoints for the DMF and NCF models.
• evaluate.py is the evaluation script for computing the NDCG and Hit Ratio metrics on the saved models.
• The results obtained with the trained models are shown in dmf_results and ncf_results.

Note: Code adapted from here and here