Navigating Molecular Networks (導航分子網絡)

Sukumar, N.

Navigating Molecular Networks
  • 出版商: Springer
  • 出版日期: 2025-01-30
  • 售價: $2,180
  • 貴賓價: 9.5 折 $2,071
  • 語言: 英文
  • 頁數: 108
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031762894
  • ISBN-13: 9783031762895

    • 尚未上市，無法訂購

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商品描述

This book delves into the foundational principles governing the treatment of molecular networks and "chemical space"--the comprehensive domain encompassing all physically achievable molecules--from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein.

Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies.

商品描述(中文翻譯)

本書深入探討了支配分子網絡和「化學空間」的基本原則——這是一個涵蓋所有物理上可實現分子的綜合領域——從向量空間、圖論和數據科學的角度出發。它探討了相似性核、網絡度量、譜圖論和隨機矩陣理論，並在這些多樣主題之間編織出引人入勝的聯繫。特別強調了分子網絡的可視化。探索繼續深入當代生成式深度學習模型，這些模型在尋求具備特定性質的新材料的過程中越來越重要，展示了該領域一些最引人注目的進展。最後，討論了發現、創造力及人工智慧（AI）在其中的角色的意義。

其主要讀者群包括專攻物理、化學和材料科學的高年級本科生和研究生。此外，本書也適合對通過計算、網絡、AI和機器學習（ML）方法轉變材料發現潛力感興趣的人士。

作者簡介

N. Sukumar is an Adjunct Professor at the School of Artificial Intelligence, Amrita Vishwa Vidyapeetham University, Coimbatore, India. He was the Founding Head and retired as Professor, Department of Chemistry, and Founding Director, Center for Informatics, at Shiv Nadar University, India. He earned his M.Sc. in Chemistry from the Indian Institute of Technology, Kanpur, and his Ph.D. from the State University of Chemistry at Stony Brook. He completed postdoctoral appointments at the University of Southern California, the University of New Orleans, and Marquette University. Sukumar was also an Alexander von Humboldt Fellow at the University of Bonn, Germany, and served as a visiting scientist at the Wadsworth Institute of the New York State Department of Health. Additionally, he worked as an Associate Research Professor at the Rensselaer Polytechnic Institute in Troy, NY.

His research spans quantum chemistry, density functional theory, computational and cheminformatic methods for discovering molecules and materials with specific chemical and biological properties. He specializes in developing novel molecular descriptors and robust property modelling methods for predicting and interpreting protein-ligand binding and protein similarity classification.

Currently, Sukumar's active research programs involve drug, polymer, and nanomaterials design through QSAR/QSPR modelling and machine learning. He also explores protein and DNA bioinformatics using structure-based methods and molecular descriptors. His work includes chemical and biological networks, employing graph and network properties to study and design molecular libraries, along with materials design using machine learning and first-principles computations to unveil complex relationships between structure and properties of materials.

Sukumar is the editor of A Matter of Density (Wiley, 2012) and co-author of Computational Drug Discovery: A Primer (IonCure Press, 2023), among other book chapters and research papers.

作者簡介(中文翻譯)

N. Sukumar 是印度科印巴托的阿姆里塔大學人工智慧學院的兼任教授。他曾擔任印度希夫納達大學化學系的創系主任並退休為教授，以及資訊中心的創始主任。他在印度理工學院坎普爾校區獲得化學碩士學位，並在石溪州立大學獲得博士學位。他曾在南加州大學、新奧爾良大學和馬凱特大學完成博士後研究。他還曾是德國波恩大學的亞歷山大·馮·洪堡研究員，並擔任紐約州衛生部瓦茲沃斯研究所的訪問科學家。此外，他在紐約州特洛伊的倫斯勒理工學院擔任副研究教授。

他的研究涵蓋量子化學、密度泛函理論、計算方法和化學資訊學方法，以發現具有特定化學和生物特性的分子和材料。他專注於開發新穎的分子描述符和穩健的性質建模方法，以預測和解釋蛋白質-配體結合及蛋白質相似性分類。

目前，Sukumar 的活躍研究計畫涉及通過 QSAR/QSPR 建模和機器學習進行藥物、聚合物和納米材料的設計。他還利用基於結構的方法和分子描述符探索蛋白質和 DNA 的生物資訊學。他的工作包括化學和生物網絡，利用圖形和網絡特性來研究和設計分子庫，並使用機器學習和第一性原理計算進行材料設計，以揭示材料結構與性質之間的複雜關係。

Sukumar 是《A Matter of Density》（Wiley, 2012）的編輯，並共同撰寫了《Computational Drug Discovery: A Primer》（IonCure Press, 2023）等書籍章節和研究論文。

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