FROM BLACK BOXES TO CLINICAL INSIGHTS: ROLE OF EXPLAINABLE AI IN PREDICTING DRUG-DRUG INTERACTIONS.

Abstract

Drug-drug interactions (DDIs) are a major obstacle for safe and effective drug use. They result in drug reactions, failure of treatment and increased health care cost. Artificial intelligence (AI) has proven to be a useful tool in predicting DDIs as the amount of chemical, pharmacological and clinical information grows rapidly. Machine learning and deep learning models are capable of handling large volumes of data to identify new and clinically significant interactions that are not identified by traditional rule-based systems. The frequent application of black-box AI approaches, however, restricts their application. It is difficult to create trust, verify results, and obtain regulatory permission without being transparent. Explainable AI (XAI) offers a promising answer, showing the way AI models come up with their predictions. This can facilitate transparency, responsibility, and acceptance of clinical settings. The attention-based graph neural networks, feature attribution techniques, including SHAP and LIME, as well as rule-based surrogate modeling, are the recent developments that assist in explaining the molecular, pharmacokinetic, and pharmacodynamic processes of DDIs. These techniques enhance our interpretation of risk scoring schemes; contribute to decision-making in clinical practice by reducing alert fatigue and confirming the possibility of mechanisms. This review gathers the recent developments in the field of XAI and DDI prediction. It dwells on new approaches, applications, and issues. It further talks about future directions, such as integrating multi-omics information, real-time monitoring, and developing standard frameworks of interpretability. XAI could potentially convert opaque computational results to clinically interpretable insights that aid precision therapeutics and patient safety.