Artificial intelligence-driven drug interaction prediction

Abstract

Artificial intelligence (AI) is developing at a rapid pace and this has led to revolutionary changes in many fields, including healthcare. Drug interaction prediction, which evaluates possible interactions between various medications to guarantee patient safety and maximize therapeutic outcomes is a crucial component of healthcare. This work investigates the use of artificial intelligence (AI) methods for predicting drug interactions, with a particular emphasis on the combination of natural language processing, knowledge graphs, and machine learning algorithms. The manual curation and experimental research that are frequently used in traditional drug interaction prediction methods limit their scalability and real-time applicability. On the other hand, artificial intelligence (AI) methods use molecular data, electronic health records, and large-scale healthcare data to improve the precision and effectiveness of drug interaction prediction. Deep neural networks and ensemble approaches are two examples of machine learning models that are essential for evaluating various datasets and spotting complex patterns related to drug interactions.