Deep learning in drug design : methods and applications / Qifeng Bai, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, PR China, Tingyang Xu, Hupan Lab, HangZhou, PR China, Alibaba DAMO Academy, HangZhou, PR China, Junzhou Huang, Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States.

Format:

Book

Contributor:

Bai, Qifeng, editor, contributor.

Xu, Tingyang, editor, contributor.

Huang, Junzhou, editor, contributor.

Language:

English

Subjects (All):

Drugs--Design.

Drugs.

Deep learning (Machine learning).

Drug Design.

Deep Learning.

Medical Subjects:

Drug Design.

Deep Learning.

Physical Description:

1 online resource (xxv, 472 pages) : illustrations (chiefly color)

Place of Publication:

London, United Kingdom ; Cambridge, MA, United States : Academic Press is an imprint of Elsevier, [2026].

Summary:

"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."--Provided by publisher.

Contents:

Part 1 Deep learning theories and methods for drug design

Chapter 1 Molecular representations in deep learning / Jian Ma, Qifeng Bai and Tingyang Xu

Chapter 2 CNNs in drug design / Yifan Niu, Tingyang Xu and Jia Li

Chapter 3 GNNs in drug design / Yahao Ding, Yiqing Shen, Kai Yi, and Yu Guang Wang

Chapter 4 RNNs and LSTM in drug design / Xiyue Zhao, Yixuan Zhou and Qifeng Bai

Chapter 5 Deep reinforcement learning in drug design / Hao Lu, Qian Wang, Kun Zhang, Zirong Huang, Xiancong Hou, Guohui Zhao, and Hao Liu

Chapter 6 Transformer and drug design / Chaohao Yuan, Tingyang Xu and Yu Rong

Chapter 7 Generative models for drug design / Yijin Zhou and Yu Guang Wang

Chapter 8 Geometric graph learning for drug design / Wenbing Huang and Jiacheng Cen

Chapter 9 Self-supervised learning for drug discovery / Zhen Li, Dongjiang Niu and Zengqian Deng

Chapter 10 Transfer learning and meta-learning for drug discovery / Long-Kai Huang

Chapter 11 Explainable artificial intelligence for drug design models / Yangyang Li, Hao Liu, Xuze Wang, Zhexuan Ding, Xingxing Yu, Jiangrui Li, Jiaming Liu, Dawei Yang, Zhiqiang Wei

Chapter 12 Large models in drug design / Qifeng Bai, Maoying Liu and Tingyang Xu

Part 2 Deep learning applications in drug design

Chapter 13 Deep learning for protein secondary structure prediction / Yuzhi Guo and Junzhou Huang

Chapter 14 Deep learning in protein structure prediction / Jiaxiang Wu

Chapter 15 Deep learning for affinity prediction and interface prediction in molecular interactions / Yatao Bian, Huaijin Wu and Junchi Yan

Chapter 16 Deep learning for complex structure prediction in molecular interactions / Yatao Bian, Nianzu Yang, Jiaxiang Wu, and Junchi Yan

Chapter 17 Deep learning in chemical synthesis and retrosynthesis / Peilin Zhao and Ziqiao Meng

Chapter 18 Deep learning for ADME prediction / Hehuan Ma and Junzhou Huang

Chapter 19 Deep learning for toxicity prediction / Yuwei Miao and Junzhou Huang

Chapter 20 Deep learning for TCR–pMHC binding prediction / Saiyang Na and Junzhou Huang

Chapter 21 Deep learning for B-cell epitope prediction and receptor–antigen binding prediction / Feng Jiang and Junzhou Huang

Chapter 22 Deep learning for antigen-specific antibody design / Bing Song and Tao Wang

Chapter 23 Ethical and regulatory of artificial intelligence in drug design / Xufei Luo, Fengxian Chen, Yaolong Chen, Qingguo Zhou.

Notes:

Includes bibliographical references and index.

Description based on online resource, publisher supplied metadata and other sources.

Other Format:

Print version: Bai, Qifeng Deep Learning in Drug Design

ISBN:

9780443329098

OCLC:

1541775107