Drug-Drug Adverse Effect Prediction with Graph Co-Attention

TL;DR

This paper introduces a neural network with a co-attention mechanism for predicting drug-drug adverse effects, leveraging drug types and molecular structures to improve early joint representation learning.

Contribution

The paper presents a novel neural network architecture with co-attention for DDI prediction, achieving state-of-the-art results using drug type and molecular data.

Findings

Achieves state-of-the-art prediction accuracy on DDI datasets.

Highlights the importance of early joint representation of drug pairs.

Demonstrates effectiveness of co-attention in modeling drug interactions.

Abstract

Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is usually done in Phase IV clinical trials, but there are still plenty which remain undiscovered when the drugs are put on the market. Such accidents have been affecting an increasing proportion of the population (15% in the US now) and it is thus of high interest to be able to predict the potential side effects as early as possible. Systematic combinatorial screening of possible drug-drug interactions (DDI) is challenging and expensive. However, the recent significant increases in data availability from pharmaceutical research and development efforts offer a novel paradigm for recovering relevant insights for DDI prediction. Accordingly, several recent approaches focus on curating massive DDI datasets (with millions of examples) and training machine learning models on them. Here we propose a neural network architecture able to set state-of-the-art results on this task---using the type of the side-effect and the molecular structure of the drugs alone---by leveraging a co-attentional mechanism. In particular, we show the importance of integrating joint information from the drug pairs early on when learning each drug's representation.