Explainable artificial intelligence (XAI) bridges the gap between machine intelligence and human understanding, a critical need in high-stakes domains like drug discovery. This chapter introduces XAI’s core concepts, methods, and applications tailored for AI-driven biomedical research. It begins by categorizing interpretability approaches into inherent (e.g., linear models and decision trees) and post hoc methods (e.g., LIME and saliency maps), while distinguishing between model-specific and model-agnostic techniques. Emphasis is placed on neural networks enhanced by attention mechanisms, where learned weights highlight molecular substructures or image regions critical to predictions—essential for validating hypotheses in drug design. The chapter further explores knowledge graph (KG)-driven XAI, leveraging paths and subgraphs to explain predictions in tasks like drug repurposing and drug-drug interaction analysis. Evaluation metrics such as AOPC, Log-odds, and fidelity quantify explanation quality, ensuring reliability in real-world applications. For AI drug discovery researchers, this chapter offers a roadmap to enhance model transparency, mitigate risks of “black-box” decisions, and accelerate validation. By integrating XAI with biomedical KGs and natural language processing, it illuminates pathways toward trustworthy, human-aligned AI systems for drug discovery.