# Comic: explainable drug repurposing via contrastive masking for interpretable connections

## TL;DR

COMIC is a new AI tool that improves drug repurposing by identifying key drug-disease interactions and biological pathways, offering better performance and explainability.

## Contribution

COMIC introduces a novel multi-channel architecture for explainable and high-performing drug repurposing predictions.

## Key findings

- COMIC achieved a 9.55% average performance improvement over existing state-of-the-art methods.
- The predictor successfully identified 21 out of 30 FDA-approved repurposed drug-disease pairs with high confidence.
- A web-based interface was developed to enable real-time drug repurposing investigations with mechanistic pathway explanations.

## Abstract

Many diseases worldwide remain untreated due to the slow and expensive process of drug development. Repurposing existing FDA-approved drugs offers a faster solution, especially with the assistance of artificial intelligence. Despite advancements in AI-driven drug repurposing, current approaches either have lackluster performance or fail to highlight the intricate pathways through which drugs act on diseases. The clinical utility of AI-driven drug repurposing remains constrained by these limitations, particularly for rare and undertreated diseases where data is scarce. To address the need for a precise and explainable predictor, this paper introduces COMIC (COntrastive Masking with Interpretable Connections), a predictor that employs a multi channel architecture consisting of a feature masking branch, which identifies critical drug-disease interaction patterns by extracting the most informative features, and a path masking branch, which highlights relevant biological pathways through which drugs exert their therapeutic effects. Comprehensive evaluation of the COMIC predictor on the PrimeKG knowledge graph (comprising 17,080 diseases, and 4 M+ relationships) with nine distinct disease area splits demonstrated a 9.55% average performance improvement over the current state-of-the-art. The practical applicability of the proposed predictor is evaluated on a set of the most recent 30 FDA-approved repurposed drug disease pairs. The COMIC predictor successfully identified 21 of these pairs with high confidence scores. To facilitate real-time drug repurposing investigations, we have developed a publicly available web-based interface for the COMIC predictor (https://sds-genetic-interaction-analysis.opendfki.de/drug_prediction/). This application takes disease names as input and returns a ranked list of potential repurposing candidates, along with predicted mechanistic pathways elucidating the drug-disease interactions.