Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review

A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an emerging approach in pharmacological research. However, the existing supervised models and deep learning-based techniques still have several limitations. This paper proposes a novel DDI analysis and prediction framework called the Multi-View Semi-supervised Graph-based (MVSG) framework, which provides a comprehensive judgment by integrating multiple DDI features and functions without any time-consuming training process. Unlike conventional approaches, MVSG can search for the most suitable similarity (or distance) measurement among DDI data and construct graph structures for each feature. By employing a parameter self-tuning strategy, MVSG fuses multiple graphs according to the contributions of features' information. The actual anticancer drug data are extracted from the authoritative public database for evaluating the effectiveness of our framework, including 904 drugs, 7730 DDI records and 19 types of drug interactions. Validation results indicate that the prediction is more accurate when multiple features are adopted by our framework. In comparison to conventional machine learning techniques, MVSG can achieve higher performance even with less labeled data and without a training process. Finally, MVSG is employed to narrow down the search for potential valuable combinations.

MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder

Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitigating the risks associated with drug administration in patients, it also helps in minimizing the likelihood of hazardous situations arising during a patient's course of treatment. However, researchers have found that there is a problem of asymmetric drug interactions, where one drug may affect another but not vice versa. This adds to the difficulty of prediction, so in polypharmacy, the order of drug administration is critical to efficacy and safety, and few current studies predict asymmetric DDIs. Aiming at the above problems, we propose a framework based on multimodal data and a variational graph autoencoder named MAVGAE for predicting asymmetric drug interactions. The framework initially encodes multimodal data into low-dimensional representations and then utilizes a variational graph autoencoder for encoding and decoding. During the model training process, supervised learning is employed for the classification task with the incorporation of heterogeneity information, ensuring accurate prediction of drug interactions. Experimental validation on a large-scale drug dataset demonstrates the framework's high accuracy and reliability in predicting non-symmetrical drug interactions, offering effective support and guidance for drug research.