Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders

TL;DR

This paper introduces an attentive multi-view graph auto-encoder framework to integrate heterogeneous drug features, improving the accuracy and interpretability of drug similarity measures for clinical applications.

Contribution

It proposes a novel multi-view graph auto-encoder with attention mechanisms to effectively combine diverse drug features, enhancing similarity learning in semi-supervised and unsupervised settings.

Findings

Significant improvement in predictive accuracy.

Enhanced interpretability through view weighting.

Better capacity to embed complex drug features.

Abstract

Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.