Abstract: The drug discovery landscape and neurodegenerative disease diagnostics are expe-riencing a paradigm shift with the integration of artificial intelligence (AI) and machine learn-ing (ML) technologies. This review provides an elaborate discussion of AI-based approaches across all stages of computer-aided drug design (CADD), including virtual screening, peptide synthesis, pharmacophore modelling, quantitative structure–activity relationships (QSAR), and drug repurposing. Particular emphasis is placed on Alzheimer's disease (AD), a multifac-eted neurodegenerative disorder, with AI enabling early diagnosis, patient stratification, and subtype classification to support precision medicine. The review highlights recent developments in supervised and unsupervised learning, omics integration, and the increasing importance of explainable AI (XAI) in addressing the “black box” limitations of traditional AI models. We categorize and assess major AI platforms and tools by their scope, stage of implementation, and form of explainability, offering a practical framework for their application in pharmaceutical and clinical research. The combined use of deep learning, quantum simulation, and virtual reality is also discussed in the context of de novo drug design, compound screening, and toxicity prediction. With con-tinued advances in AI, interdisciplinary collaboration and ethical oversight are essential to translate these digital innovations into effective, safe, and personalized therapies. This review serves as an all-encompassing guide for researchers, clinicians, and developers working at the interface of AI and drug development, particularly those addressing the unmet challenges in Alzheimer's disease. As outlined in the abstract and reflected in the article title, this review bridges AI-driven drug discovery and Alzheimer’s diagnosis, with specific empha-sis on explainable models for next-generation therapeutics. The subsequent sections expand on these themes, offering a structured synthesis of tools, applications, and methodological frameworks.
Thakur et al. (Tue,) studied this question.