Drug discovery and development pipelines are timely consuming and expensive, depending on numerous factors. Artificial intelligence (AI) tools are increasingly being applied in drug discovery for Alzheimer’s disease (AD). In the “big data” era, AI offers cutting-edge applications of informatics and computational tools for re-defining disease biology, discovering new therapeutics, and identifying novel targets with the least errors. The application of AI has the potential to enhance the pipeline across all stages of drug discovery and reduce failure rates in drug development for AD. In this chapter, we introduce AI techniques accessible for accelerating drug discovery. We summarize representation learning, machine learning, and deep learning toolboxes, available for drug discovery. We illustrate the application of AI for target identification, evaluation of pharmacokinetic properties (i.e., brain penetration), safety, and identification of biomarkers in clinical trials. We discuss current challenges and future directions of AI-based solutions for drug discovery. Rapidly developing, powerful and innovative AI technologies can expedite drug discovery and development for AD.

Alzheimer’s disease (AD) drug development is a complex process that proceeds from identification of a biological target; to testing of candidate therapies in in vitro assays; assessment of efficacy in animal models and assessment of safety in several animal species; clinical testing in humans in Phase1, Phase 2, and Phase 3 clinical trials; regulatory review by agencies in all countries in which the drug might be marketed; and eventual commercialization. This process requires more than a decade to accomplish. The process involves substantial infrastructure resources; multiple stakeholders; and funding from a variety sources along the developmental pathway. This is the complex ecosystem that supports AD drug development.

While Alzheimer’s disease (AD) remains one of the very few common chronic diseases of aging and old age without any effective treatments to slow or prevent the illness, an historical perspective can provide the context for why this is true. The same historical perspective demonstrates that this lag is primarily a frame shift in time, rather than due to excess difficulties in developing drugs for the disease. Indeed, the historical perspective suggests that today, we are “right on time” for an explosion in new drugs for the disease after 40 years of rapid progress in basic and clinical research. The first drugs (cholinergic agents) for AD were approved after only about 15-20 years of research in the field, including the time needed for basic discovery, clinical development and regulatory approval.  Today, after 40 years of AD research, many disease-modifying drugs are in clinical development. Considering that cancer, diabetes and hypertension research began more than 80-100 years ago, the rapid progress in AD research can actually be viewed as impressive progress.