Synergizing Convolutional Neural Networks and Drug Similarity Estimation for Improved Drug-Drug Interaction Prediction

Abstract

Drug-drug interactions can cause adverse effects and impact patient safety. Traditionally, the prediction of DDIs has been labor intensive. However, these approaches have certain limitations in terms of scalability, convergence of different drug combinations, and capturing complex interactions. The potential combinations increase as the number of approved drugs increases; this results in the need for efficient methods. This research aims to utilize artificial intelligence and machine learning techniques to develop a method that accurately detects potential DDIs. The proposed idea allows for greater accuracy and automation in predicting DDI, reducing the time and cost required. The proposed method takes two drug formulas as input and employs a Drug Feature Extractor to extract their features. These features are then represented in feature maps, which are used when calculating a similarity score between input drugs and other drugs in the data set, and to predict potential DDIs. The model combines a drug similarity calculator and DDI predictor, enabling the system to process data in a “human-like” method; aiding in predicting interactions for newly developed drugs. The proposed model achieved state-of-art performance with an accuracy of 88.9%. The results demonstrate the efficacy of the proposed method in predicting potential drug interactions.