Artificial Intelligence for Drug Design (eng)

The subject of this book is drug design using artificial intelligence (AI). It mainly covers the area of machine learning, deep learning and their applications in drug development, target identification and structure prediction. As the rapid development of AI technologies and continuous accumulation of biomedical data, AI has been widely employed in the pharmaceutical field, dramatically accelerating the process of drug discovery. AI can rapidly mine high information density data from huge amounts of raw data, providing more new insights by integrating and analysing these data. With numerous research teams and major pharmaceutical companies actively strategizing their drug development plans based on artificial intelligence, there is promising potential for breakthroughs in the progress of 'First-in-Class' novel drug research and development in China.

This book systematically introduces the professional knowledge of artificial intelligence and its applications in various aspects of the pharmaceutical field. The overall structure of the book is progressive, starting from the basics and gradually delving into more advanced topics. The content is layered and progressive, with strong logical and systematic connections between chapters. Each chapter not only provides detailed explanations of the principles, algorithms, and models of artificial intelligence but also closely relates them to practical cases in the pharmaceutical field and drug development. This approach caters to the knowledge needs of readers with different professional backgrounds. In summary, this book not only focuses on the present but also provides readers with foundational knowledge in the field of artificial intelligence in pharmacy, helping them smoothly enter this rapidly advancing frontier of science

Honglin Li, from Innovation Center for AI and Drug Discovery, East China Normal University. Dr. Honglin Li has dedicated significant time to addressing challenging, cutting-edge scientific problems in drug design and target discovery. He has developed more than ten drug design methods and software primarily focusing on methodological development and applied them to discover new targets and design promising compounds. The representative drug design methods and software suites include the graphical drug design software eSHAFTS and ePharmer, pioneered in China. Target discovery methods like PharmMapper and ChemMapper are popular, boasting a global user base of over 35,000. He has also developed several AI-based drug design methods, such as disease-target knowledge graph e-TSN, near-drug space exploration method CIRS, macrocyclic drug design method MacFormer, and online drug design platform iDrug. He has published over 210 papers in prestigious journals such as Nat. Commun., NAR, Adv. Sci., PNAS, STTT, Engineering, JMC, and other professional publications, accumulating more than 8,000 citations; filed applications for more than 118 invention patents (including 54 domestic authorizations and 13 foreign authorizations) and 15 software copyrights. He has transferred six drug candidates to pharmaceutical companies for pre-clinical research, and three of these drugs progressed to clinical trials.

Mingyue Zheng, from Shanghai Institute of Materia Medica, Chinese Academy of Sciences. Dr. Mingyue Zheng received his Ph.D. degree from Shanghai Institute of Materia Medica (SIMM), Chinese Acadamy of Sciences in 2006, majoring in computational drug design. He currently works as a Professor in State Key Laboratory of Drug Research at SIMM, where he focuses on artificial intelligence approaches for rational drug design and discovery. His research interests also encompass multidisciplinary studies in the fields of medicinal chemistry, cheminformatics, and computational biology. He has been engaged in the machine-learning based methodology development around the discovery and structural optimization of lead compounds, the assessment of drug ADME/T properties, as well as the application of the methods in practical drug design and discovery process. Till now, he has published more than 200 papers in Nat Comput Sci, Immunity, Nat Commun, Trends Pharmacol Sci, Circ Res, Protein & Cell, Nucleic Acids Res, etc.