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Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
Khang Ngo · Truong Son Hy · Risi Kondor

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in AI for Science: Progress and Promises »
Event URL: https://openreview.net/forum?id=Hnax-9OBNtH »

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.

Keywords: Drug Discovery · graph variational autoencoders · drug-drug interaction prediction · multimodal graph

Author Information

Khang Ngo (FPT Software Company Limited, FPT Cau Giay building, Duy Tan street, Dich Vong Hau ward, Cau Giay district, Hanoi)
Truong Son Hy (The University of Chicago)

Research areas: Kernel methods, Graph kernels, Graph Neural Networks

Risi Kondor (Flatiron Institute)

Risi Kondor joined the Flatiron Institute in 2019 as a Senior Research Scientist with the Center for Computational Mathematics. Previously, Kondor was an Associate Professor in the Department of Computer Science, Statistics, and the Computational and Applied Mathematics Initiative at the University of Chicago. His research interests include computational harmonic analysis and machine learning. Kondor holds a Ph.D. in Computer Science from Columbia University, an MS in Knowledge Discovery and Data Mining from Carnegie Mellon University, and a BA in Mathematics from the University of Cambridge. He also holds a diploma in Computational Fluid Dynamics from the Von Karman Institute for Fluid Dynamics and a diploma in Physics from Eötvös Loránd University in Budapest.

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