The Role of Explainable Artificial Intelligence (XAI) in Drug Discovery: A Study of Opportunities and Barriers to Implementation

Abstract

Drug discovery is a complex, lengthy, and costly process with a high failure rate, especially during clinical trials. The integration of Artificial Intelligence (AI) has revolutionized various stages of drug discovery by enabling faster and more accurate analysis of biological and chemical data. However, most AI models in this field operate as “black boxes,” where their decision-making processes are opaque and difficult to interpret. This lack of transparency poses significant challenges in terms of trust, validation, and adoption of AI-generated predictions in both clinical and regulatory settings. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a promising approach to improve the interpretability of AI models without compromising their predictive power. This study aims to systematically review the opportunities, challenges, and future directions of XAI implementation in drug discovery. Using a qualitative method with a systematic literature review approach, data were collected from reputable databases including Scopus, PubMed, IEEE Xplore, SpringerLink, and ScienceDirect, focusing on publications from 2018 to 2024. The analysis identified five main themes: the role of XAI in molecular target identification, application of XAI in compound screening and molecular structure optimization, interpretation of drug toxicity predictions, challenges in XAI implementation, and future research directions. XAI techniques such as SHAP and LIME have proven useful in explaining AI model predictions, improving biological validation, and enabling more informed decision-making by scientists. However, significant challenges remain, including the trade-off between interpretability and accuracy, lack of universal standards, and the complexity of modeling biological systems. This study highlights the critical need for developing standardized interpretability frameworks, user-friendly interfaces, and collaborative environments between data scientists and healthcare professionals to foster XAI adoption in real-world drug discovery processes. Ultimately, XAI has the potential to increase transparency, trust, and efficiency, paving the way for safer and more effective therapeutic developments.