Deep Learning in Drug Design

Qifen Bai

ISBN 10: 0443329087 / ISBN 13: 9780443329081

Editore: Elsevier Inc Okt 2025, 2025

Riguardo questo articolo

Neuware - Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, 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, etc., as well as deep learning applications in various aspects of drug design. This book offers a comprehensive academic overview of deep learning in drug design. It begins with molecular representations, CNNs, GNNs, Transformers, generative models, explainable AI, large models, etc. Next, it covers deep learning applications like protein structure prediction, molecular interactions, ADMET prediction, antibody design, and so on. Finally, a separate chapter is dedicated to the introduction of the ethics and regulation of artificial intelligence in drug design. This book is ideal for readers aiming to learn and implement deep learning methods and applications in drug design and related fields.Deep Learning in Drug Design: Methods and Applications is particularly helpful to undergraduate, graduate, and doctoral 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.

Codice articolo 9780443329081

Dati bibliografici

• Titolo: Deep Learning in Drug Design
• Editore: Elsevier Inc Okt 2025
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 0443329087
• ISBN 13: 9780443329081
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 191 A x 25 P (millimetri) 235 millimetri di larghezza per 191 millimetri di altezza per 25 millimetri di profondità
• Peso: 1028 grammi

Descrizione articolo

Riassunto

Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, 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, etc., as well as deep learning applications in various aspects of drug design. This book offers a comprehensive academic overview of deep learning in drug design. It begins with molecular representations, CNNs, GNNs, Transformers, generative models, explainable AI, large models, etc. Next, it covers deep learning applications like protein structure prediction, molecular interactions, ADMET prediction, antibody design, and so on. Finally, a separate chapter is dedicated to the introduction of the ethics and regulation of artificial intelligence in drug design. This book is ideal for readers aiming to learn and implement deep learning methods and applications in drug design and related fields.
Deep Learning in Drug Design: Methods and Applications is particularly helpful to undergraduate, graduate, and doctoral 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.

Informazioni sugli autori

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 is a Senior Researcher in AI for Science Group at DAMO Academy, Alibaba, and Hupan Lab since 2024. He earned his Master's degree and Ph.D. from University of Connecticut and his Bachelor's degree from Shanghai Jiaotong University. His research encompasses deep learning applications for de novo drug design, generation of medical images, and AI for Science. His work has been published in top-tier data mining and machine learning conferences, including NeurIPS, ICML, SIGKDD, VLDB, Nature Communications (NC), Internet of Things (IoT), and Annuals of Surgery. Additionally, Dr. Xu has served as a reviewer for prestigious conferences and journals, 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.