Artificial Intelligence for Drug Design
暫譯: 藥物設計的人工智慧

Li, Honglin, Zheng, Mingyue, Zhu, Feng

Artificial Intelligence for Drug Design
  • 出版商: Springer
  • 出版日期: 2026-01-01
  • 售價: $21,890
  • 貴賓價: 9.5 折 $20,796
  • 語言: 英文
  • 頁數: 916
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819525241
  • ISBN-13: 9789819525249
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book focuses on the application of artificial intelligence in drug research and development, particularly its growing role in evaluating interactions between biological targets and drug molecules and optimizing drug design pathways. It offers a comprehensive structure divided into four parts: fundamentals of AI algorithms, data foundations and representations, AI driven drug design, and program code. The book systematically introduces key AI methodologies, highlights essential biomedical data resources, and presents data mining approaches based on artificial intelligence. Following the workflow of drug R&D, each chapter explains the basic principles and challenges of specific drug design steps and then reviews the corresponding advances in AI algorithms, supplemented by cross-disciplinary application examples. Readers will gain a clear understanding of how AI integrates into and accelerates the drug development process while reducing associated risks and costs, making the book particularly valuable for researchers and technical professionals engaged in life sciences and pharmaceutical R&D.

商品描述(中文翻譯)

本書專注於人工智慧在藥物研究與開發中的應用，特別是其在評估生物靶點與藥物分子之間相互作用及優化藥物設計路徑中的日益重要角色。書籍結構全面，分為四個部分：人工智慧演算法的基本原理、數據基礎與表示、人工智慧驅動的藥物設計，以及程式碼。書中系統性地介紹了關鍵的人工智慧方法，突顯了重要的生物醫學數據資源，並呈現基於人工智慧的數據挖掘方法。根據藥物研發的工作流程，每一章節解釋了特定藥物設計步驟的基本原則和挑戰，並回顧了相應的人工智慧演算法進展，輔以跨學科的應用範例。讀者將清楚了解人工智慧如何整合並加速藥物開發過程，同時降低相關風險和成本，使本書對於從事生命科學和製藥研發的研究人員及技術專業人士特別有價值。

作者簡介

Honglin Li Dean of the School of Pharmacy at East China Normal University and Director of the Innovation Center for AI and Drug Discovery. His research focuses on the development and application of computational methodologies for drug discovery and target identification, integrating artificial intelligence with experimental and theoretical approaches.

Mingyue Zheng Professor and Principal Investigator at the Shanghai Institute of Materia Medica, Chinese Academy of Sciences. His work focuses on the development of artificial intelligence and big data-driven drug design technologies, including methods for biomedical big data mining, AI-powered precision drug design, and the discovery of novel targets and drug candidates.

Feng Zhu Professor at the College of Pharmaceutical Science, Zhejiang University. His research focuses on identifying the druggability of therapeutic drug targets by leveraging AI and OMICs, developing innovative computational methods and online tools for drug target discovery, and investigating the mechanisms between drugs and key biological targets.

Fang Bai Associate Professor jointly appointed in the School of Life Science and Technology and the Shanghai Institute for Advanced Immunochemical Studies at ShanghaiTech University. Her research focuses on developing advanced computational methods for drug design that integrate artificial intelligence with physical modeling. Recently, her work has addressed challenging drug targets--such as protein-protein interactions--by designing innovative therapeutic strategies, including molecular glues and PROTACs (proteolysis-targeting chimeras).

作者簡介(中文翻譯)

李洪林 東華師範大學藥學院院長及人工智慧與藥物發現創新中心主任。他的研究專注於藥物發現和靶點識別的計算方法的開發與應用，將人工智慧與實驗和理論方法相結合。 鄭明月 中國科學院上海藥物研究所教授及首席研究員。他的工作專注於開發基於人工智慧和大數據驅動的藥物設計技術，包括生物醫學大數據挖掘、人工智慧驅動的精準藥物設計，以及新靶點和藥物候選物的發現。 朱峰 浙江大學藥學院教授。他的研究專注於利用人工智慧和OMICs識別治療藥物靶點的可藥性，開發創新的計算方法和在線工具以進行藥物靶點發現，並研究藥物與關鍵生物靶點之間的機制。 白芳 上海科技大學生命科學與技術學院及上海高級免疫化學研究所的副教授。她的研究專注於開發將人工智慧與物理建模相結合的先進藥物設計計算方法。最近，她的工作針對具有挑戰性的藥物靶點——如蛋白質-蛋白質相互作用——設計創新的治療策略，包括分子膠和PROTACs（蛋白質降解靶向嵌合體）。

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