Deep Learning in Drug Design

Name: Deep Learning in Drug Design
Price: 1587.00 SEK
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Author: Qifeng Bai, Tingyang Xu, Junzhou Huang
ISBN: 9780443329081

Methods and Applications

av Qifeng Bai, Tingyang Xu, Junzhou Huang

Häftad, Engelska, 2025-10-01

1587

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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.

Qifeng Bai is a professor in School of Basic Medical Sciences of Lanzhou University. He is also an associate editor in the journal named Frontiers in Chemistry. He is interested in drug design by developing new algorithms, software, machine learning, and deep learning. He is also good at conformation transition studies of receptors (e.g. kinases and G protein-coupled receptors) by performing molecular dynamics simulations. He has developed the software MolAICal which has been widely used to design drugs based on deep learning and traditional algorithms. Tingyang Xu, a Senior Researcher at Tencent AI Lab's AI for Science Center, earned his Ph.D. from The University of Connecticut and his Bachelor's degree from Shanghai Jiaotong University. His research encompasses deep learning applications for de novo drug design, molecular property prediction, and molecular dynamics simulation, and he has significantly contributed to the development of advanced graph neural networks for tasks like subgraph recognition and node classification. His work has been published in top-tier data mining and machine learning conferences, including NeurIPS, ICML, SIGKDD, AAAI, VLDB, the Journal of Parallel and Distributed Computing (JPDC), Internet of Things (IoT), and Annuals of Surgery. Additionally, Dr. Xu has served as a reviewer for prestigious conferences and journals, including ICML, NeurIPS, KDD, WWW, AAAI, PR, and TKDE, and as the Industrial Track Chair for BIBM 2019. Junzhou Huang is the Jenkins Garrett Professor in the Computer Science and Engineering department at the University of Texas at Arlington. He received the Ph.D. degree in Computer Science at Rutgers, the State University of New Jersey. His major research interests include machine learning, computer vision, medical image analysis, and bioinformatics. His research has been recognized by several awards including UT STARs Award, NSF CAREER Award, Google TensorFlow Model Garden Award, IBM Watson Emerging Leaders, four Best Paper Awards (MICCAI'10, FIMH'11, STMI'12, and MICCAI'15) as well as two Best Paper Nominations (MICCAI'11 and MICCAI'14). He is a Fellow of AIMBE.