Published online by Cambridge University Press: 03 March 2022
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.
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