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Drug discovery and development is a data-intensive field. As an emerging technology capable of handling big data and uncovering complex interaction patterns, artificial intelligence has made breakthrough progress in the field of drug discovery and pharmaceutical research. The application of artificial intelligence in drug discovery and development can be traced back to Hansch’s quantitative structure-activity relationship proposed in 1964. Today, it is widely used in various new drug research and development stages, including drug target discovery, compound screening, drug target interaction networks, lead compound optimization, drugability analysis, and peptide design. Artificial intelligence-assisted drug discovery (AIDD) has now become a new strategy for pharmaceutical companies. New drug research and development based on artificial intelligence is regarded by the industry as the most transformative research technology. However, the application of artificial intelligence in drug discovery and development requires the integration of expert knowledge from multiple fields such as artificial intelligence, life science, drug discovery and chemistry, which makes it difficult for newcomers to the field to find suitable reference materials. The authors of this book have been engaged in scientific research in the field of artificial intelligence-assisted drug discovery for a long time. They have accumulated a wealth of technical experience in the research and development of small molecule drugs and peptide vaccines, and has published related papers in authoritative journals, which have been highly praised by domestic and foreign peers. The protein-protein docking method developed by them has achieved the top three positions in the international CASP-CAPRI competition. After years of accumulation, based on some thoughts on the development of this field and the need to cultivate students and share knowledge, the author wrote this book.