A Novel Deep Learning Model for Drug-Drug Interactions

Abstract

Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy, emphasizing the need for accurate prediction and understanding of these interactions. This paper presents an innovative approach for DDI prediction, employing two distinct message-passing neural network (MPNN) models, each meticulously tailored to focus on one drug within a pair. By adeptly capturing the unique attributes and interactions of each drug, this novel method strives to enhance the accuracy of DDI prediction. Furthermore, we integrate the outcomes of individual MPNN models with information derived from both drugs and their molecular features. In addition to this, we harnessed a curated list of 82 existing FDA-approved drugs, which were evaluated against five vital SARS-CoV-2 proteins. Based on the Combined Score, we systematically scrutinized a selection of the top-10 drugs exhibiting the highest binding affinity to these proteins, employing a sophisticated deep learning architecture for predicting Drug-Drug Interactions. The results remarkably demonstrated the effectiveness of our approach, boasting an impressive accuracy of 0.92, an area under the curve (AUC) of 0.99, and an F1-score of 0.85. Moreover, the identified list of high-affinity drugs against SARS-CoV-2 proteins may potentially serve as a pivotal starting point for the development of new and invaluable pharmaceutical interventions.