Applied Artificial Intelligence for Drug Discovery: From Data-Driven Insights to Therapeutic Innovation
暫譯: 應用人工智慧於藥物發現：從數據驅動的洞察到治療創新

Lavecchia, Antonio

Applied Artificial Intelligence for Drug Discovery: From Data-Driven Insights to Therapeutic Innovation
  • 出版商: Springer
  • 出版日期: 2026-01-10
  • 售價: $5,780
  • 貴賓價: 9.5 折 $5,491
  • 語言: 英文
  • 頁數: 842
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031980212
  • ISBN-13: 9783031980213
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

The integration of artificial intelligence (AI) into pharmaceutical research has redefined the landscape of drug discovery, enabling unprecedented advances across data integration, molecular design, clinical translation, and therapeutic innovation.

Applied Artificial Intelligence for Drug Discovery is a comprehensive and forward-looking volume that explores how AI, machine learning (ML), and deep learning (DL) are revolutionizing the discovery and development of new drugs. Spanning 27 chapters authored by leading international experts, this book presents state-of-the-art methods and practical applications covering the entire drug discovery pipeline.

Topics include AI-based drug target identification, pathway analysis, structure- and ligand-based drug design, generative models for de novo design, peptide discovery, ADMET prediction, retrosynthesis, drug repurposing, and nanomedicine. Dedicated chapters focus on the implementation of large language models, contrastive and few-shot learning, quantum machine learning, federated and explainable AI, and clinical trial optimization.

With its balance of foundational theory, applied case studies, and emerging perspectives, the book offers a unique resource for computational chemists, pharmaceutical scientists, bioinformaticians, data scientists, and R&D professionals.

This volume serves not only as a scientific reference but also as a strategic guide for those looking to adopt AI in pharmaceutical pipelines and therapeutic development. It is equally suited for academic researchers and industrial innovators seeking to unlock the full potential of AI in healthcare.

商品描述(中文翻譯)

人工智慧（AI）在製藥研究中的整合重新定義了藥物發現的格局，使得在數據整合、分子設計、臨床轉化和治療創新方面取得了前所未有的進展。《應用人工智慧於藥物發現》（Applied Artificial Intelligence for Drug Discovery）是一本全面且具前瞻性的著作，探討了AI、機器學習（ML）和深度學習（DL）如何徹底改變新藥的發現與開發。該書由國際領先的專家撰寫，共27章，介紹了涵蓋整個藥物發現流程的最先進方法和實用應用。

書中主題包括基於AI的藥物靶點識別、途徑分析、基於結構和配體的藥物設計、用於新穎設計的生成模型、肽的發現、ADMET預測、逆合成、藥物再利用以及納米醫學。專門的章節聚焦於大型語言模型的實施、對比學習和少量學習、量子機器學習、聯邦學習和可解釋的AI，以及臨床試驗優化。

本書在基礎理論、應用案例研究和新興觀點之間取得了平衡，為計算化學家、製藥科學家、生物信息學家、數據科學家和研發專業人士提供了獨特的資源。這本書不僅作為科學參考，還作為希望在製藥流程和治療開發中採用AI的戰略指南。它同樣適合學術研究者和工業創新者，幫助他們發掘AI在醫療保健中的全部潛力。

作者簡介

Prof. Antonio Lavecchia is Full Professor of Medicinal Chemistry at the University of Naples Federico II (Italy), where he leads the Drug Discovery Laboratory and serves as Scientific Director of the Molecular Modeling Excellence Laboratory (LMM). He received his Ph.D. in Pharmaceutical Sciences from the University of Catania in 1999, completing part of his doctoral research at the University of Minnesota (USA).

With a strong background in both experimental and computational medicinal chemistry, Prof. Lavecchia is internationally recognized for his interdisciplinary expertise in drug design, molecular modeling, and the application of artificial intelligence (AI) in pharmaceutical research. His scientific work spans the development of novel algorithms, AI-based frameworks, and modeling platforms for accelerating the discovery and optimization of bioactive compounds across therapeutic areas such as oncology, metabolic diseases, inflammation, infectious diseases, and rare disorders. His academic output includes over 180 scientific publications in high-impact international journals, five books and book chapters, six patents, and over 280 conference presentations worldwide. He serves on the editorial boards of several international scientific journals and regularly acts as a peer reviewer and expert evaluator for major funding agencies and research institutions worldwide.

Prof. Lavecchia ranks among the world's top 2% of scientists (Stanford University ranking) and is acknowledged as a global expert in PPAR nuclear receptor pharmacology and AI-driven drug discovery. He is co-founder of two biotech spin-offs and has been featured on the covers of J. Chem. Inf. Model. and ACS Omega for his pioneering contributions to AI in drug discovery.

Through this volume, Prof. Lavecchia brings together leading experts in the field to provide a comprehensive, forward-thinking resource that explores the transformative role of AI across the entire drug discovery continuum.

作者簡介(中文翻譯)

安東尼奧·拉維基亞教授（Prof. Antonio Lavecchia）是義大利那不勒斯費德里科二世大學（University of Naples Federico II）藥物化學的全職教授，負責藥物發現實驗室並擔任分子建模卓越實驗室（LMM）的科學主任。他於1999年在卡塔尼亞大學（University of Catania）獲得藥學博士學位，並在美國明尼蘇達大學（University of Minnesota）完成部分博士研究。

拉維基亞教授擁有強大的實驗和計算藥物化學背景，因其在藥物設計、分子建模及人工智慧（AI）在藥物研究中的應用方面的跨學科專業知識而享有國際聲譽。他的科學工作涵蓋了新算法、基於AI的框架和建模平台的開發，旨在加速生物活性化合物的發現和優化，涉及的治療領域包括腫瘤學、代謝疾病、炎症、傳染病和罕見疾病。他的學術成果包括在高影響力的國際期刊上發表超過180篇科學論文、五本書籍及書章、六項專利，以及在全球超過280場會議上的演講。他擔任多本國際科學期刊的編輯委員會成員，並定期擔任主要資助機構和研究機構的同行評審和專家評估。

拉維基亞教授在全球科學家中排名前2%（斯坦福大學排名），並被認可為PPAR核受體藥理學和基於AI的藥物發現的全球專家。他是兩家生技新創公司的共同創辦人，並因其在藥物發現中對AI的開創性貢獻而被《化學資訊與模型期刊》（J. Chem. Inf. Model.）和《ACS Omega》雜誌封面報導。

通過本書，拉維基亞教授匯集了該領域的領先專家，提供了一個全面且前瞻性的資源，探討AI在整個藥物發現過程中的變革性角色。

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