Predicting Drug-Drug Interactions using Deep Generative Models on Graphs

TL;DR

This paper introduces a variational graph autoencoder approach that models flexible latent spaces for predicting drug-drug interactions, enhanced by molecular fingerprints, achieving competitive results on multimodal networks.

Contribution

The paper presents a novel variational graph autoencoder method with flexible latent spaces and a new feature concatenation technique for improved drug-drug interaction prediction.

Findings

Effective modeling of multimodal networks with VGAE

Enhanced link prediction using molecular fingerprints

Competitive results on drug-protein and drug-cell line networks

Abstract

Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on two multimodal networks: (1) a multi-graph consisting of drug and protein nodes, and (2) a multi-graph consisting of drug and cell line nodes. Our source code is publicly available at https://github.com/HySonLab/drug-interactions.