Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, applications, and technological advances of deep learning for drug design, which mainly consists of molecular representations, the architectures of deep learning, geometric deep learning, large models for drugs, and the deep learning applications in various aspects of drug design. This book will give readers an intuitive and simple understanding of the encoding and decoding of drugs for model training, while deep learning methods profile the different training perspectives for drug design including sequence-based, 2D, and 3D drug design based on geometric deep learning. This book is suitable for readers who are seeking to learn and use deep learning methods and applications for drug discovery and other related fields. Deep Learning in Drug Design: Methods and Applications is particularly helpful to graduate students in need of a practical guide to the principles of the discipline. Established researchers in the area will benefit from the detailed case studies and algorithms presented. - Introduces the basic theories, current methods, and cases of deep learning for drug design - Presents the major application fields of drug design based on deep learning including protein folding, retrosynthesis prediction, molecular docking, and ADMET prediction, among others - Explains the artificial intelligence of deep learning for drug design models