An Effective Model for Drug-Drug Interactions Prediction in Cold-start Scenario via Counterfactual Data Augmentation

TL;DR: A novel data augmentation approach for the prediction of DDIs in cold-start scenarios that leverages counterfactual inference to generate meaningful pseudo samples for drugs with limited prior information and enhances the understanding of drug characteristics through a meta-path-based fusion mechanism.

Abstract: Drug-drug interaction (DDI) pertains to the occurrence where the concomitant use of two or more drugs may lead to interactions in terms of their pharmacokinetic or pharmacodynamic behavior, resulting in unexpected effects. Accurately predicting DDIs holds significant importance in ensuring drug safety. Despite the numerous approaches proposed for DDI prediction, a majority of these methods often overlook the challenge presented by cold-start scenario, consequently limiting their applicability. This paper presents a novel data augmentation approach for the prediction of DDIs in cold-start scenarios. This method leverages counterfactual inference to generate meaningful pseudo samples for drugs with limited prior information. To achieve this, a HIN relevant to DDIs is initially established by amalgamating various associations between drugs and proteins. Subsequently, the identification of drug communities within this HIN is regarded as a form of counterfactual inference treatment, facilitating the generation of counterfactual links for cold-start drugs and thereby augmenting the training dataset. Lastly, we enhance our understanding of drug characteristics through a meta-path-based fusion mechanism, ultimately improving the accuracy of DDIs prediction in cold-start scenarios. We substantiate the effectiveness of our proposed method through an extensive series of experiments.