Drug discovery is a data-driven, knowledge-intensive, and labor-intensive field. Artificial intelligence (AI) has emerged as a powerful technology, capable of processing vast datasets and uncovering complex interactions. AI's application in pharmaceutical research and development (R&D) dates back to 1964, when Hansch introduced quantitative structure-activity relationships (QSAR). Since 2012, AI-assisted drug design has rapidly advanced with the rise of deep learning, now widely used across drug development stages, including target discovery, compound screening, lead optimization, drug-likeness analysis, and peptide design. AI-assisted drug design is increasingly seen as a key strategy in pharmaceutical R&D. Despite its potential, applying AI in pharmaceutical R&D requires integrating expertise from AI and drug research, posing challenges for newcomers due to its interdisciplinary nature. To address these challenges and meet the growing demand for educational resources, the authors wrote this book. The book consists of 18 chapters, organized into three sections: Basic Methods of Artificial Intelligence - This section covers core AI concepts, focusing on machine learning and deep learning methods such as support vector machines, decision trees, ensemble learning, random forests, k-nearest neighbors, and neural networks. The goal is to provide readers with foundational AI knowledge. Programming Development Environment - This section introduces Python and focuses on practical applications in pharmaceutical R&D. Readers will learn to set up deep learning frameworks like TensorFlow and PyTorch for AI-driven drug research. Fundamentals of AI-Assisted Drug Design - This section explores drug and protein databases, QSAR models, drug screening methods, molecular feature extraction, property prediction, and protein-ligand binding predictions, with practical examples illustrating AI's role in drug design.