Activity Cliffs: Where Qsar Predictions Fail
暫譯: 活性懸崖：QSAR 預測失敗的地方

Name: Activity Cliffs: Where Qsar Predictions Fail
Price: 2441 TWD
Availability: OnlineOnly
Author: Roy, Kunal, Banerjee, Arkaprava
ISBN: 3032100801

Roy, Kunal, Banerjee, Arkaprava

出版商: Springer
出版日期: 2026-01-03
售價: $2,570
貴賓價: 9.5 折 $2,441
語言: 英文
頁數: 81
裝訂: Quality Paper - also called trade paper
ISBN: 3032100801
ISBN-13: 9783032100801
相關分類: Data-mining

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商品描述

This brief introduces the readers of predictive cheminformatics to the concept of cliffs in the structure-activity landscape, which may greatly affect the data set modelability and the quality of predictions, hence generating disappointment from the performance of Quantitative Structure-Activity Relationship (QSAR) models. Although QSAR models are based on the assumption of a smooth activity landscape, where similar molecules are expected to have similar activities, some similar molecules can occasionally exhibit large differences in activity (for example, 100-fold). The definition of similarity for identifying activity cliffs may be based on chemical fingerprints or descriptors (classical activity cliffs), substructures (chirality cliffs, matched molecular pair cliffs), three-dimensional structure-based cliffs (3D cliffs), or the target-set-dependent potency difference. Some prediction outliers, even within the applicability domain of QSAR models, may arise due to the activity cliff (AC) behavior. In addition to compound pairs, activity cliffs may also be visualized in coordinated networks forming AC clusters. Despite using high-quality data, the data set's modelability may be significantly compromised in the presence of ACs, among other factors. The modelability of the dataset has been studied using different approaches like modelability index (MODI), weighted modelability index (WMODI), rivality index, etc. At the same time, the applicability domain of QSAR models is evaluated using a variety of methods, including leverage, principal components, standardization methods, and distance to the model in X-space, among others. Different methods for identifying activity cliffs have been proposed, such as the structure-activity landscape index (SALI), the structure-activity relationship (SAR) index, and the structure-activity similarity (SAS) maps. Recently, the Arithmetic Residuals in K-Groups Analysis (ARKA) has been shown to be successful in identifying activity cliffs. This approach has also been applied in small data set classification modeling. A multiclass ARKA approach has also been developed for its possible application in regression-based problems by integrating it with the quantitative read-across structure-activity relationship (q-RASAR) framework. This book showcases the evolution and the current status of the concept of activity cliffs as relevant to QSAR predictions and indicates the future directions in the research on activity cliffs. Researchers in the fields of medicinal chemistry, predictive toxicology, nanosciences, food science, agricultural sciences, and materials informatics should benefit from the concept of activity cliffs, impacting model-derived predictions.

商品描述(中文翻譯)

這篇簡介向預測化學資訊學的讀者介紹了結構-活性景觀中的懸崖（cliffs）概念，這可能會大大影響數據集的模型可建性和預測質量，從而導致對定量結構-活性關係（Quantitative Structure-Activity Relationship, QSAR）模型性能的失望。儘管QSAR模型是基於平滑的活性景觀假設，預期相似的分子會有相似的活性，但某些相似的分子有時會顯示出活性上的巨大差異（例如，100倍）。用於識別活性懸崖的相似性定義可能基於化學指紋或描述符（經典活性懸崖）、子結構（手性懸崖、匹配分子對懸崖）、基於三維結構的懸崖（3D懸崖），或目標集依賴的效能差異。即使在QSAR模型的適用範圍內，一些預測異常值也可能由於活性懸崖（AC）行為而產生。除了化合物對，活性懸崖還可以在協調網絡中可視化，形成AC集群。儘管使用高質量數據，但在存在AC等因素的情況下，數據集的模型可建性可能會受到顯著影響。數據集的模型可建性已使用不同的方法進行研究，如模型可建性指數（modelability index, MODI）、加權模型可建性指數（weighted modelability index, WMODI）、競爭指數等。同時，QSAR模型的適用範圍也使用多種方法進行評估，包括杠桿（leverage）、主成分（principal components）、標準化方法和X空間中的距離等。已提出不同的方法來識別活性懸崖，例如結構-活性景觀指數（structure-activity landscape index, SALI）、結構-活性關係（structure-activity relationship, SAR）指數和結構-活性相似性（structure-activity similarity, SAS）地圖。最近，K組分析中的算術殘差（Arithmetic Residuals in K-Groups Analysis, ARKA）已被證明在識別活性懸崖方面成功。這種方法也已應用於小數據集的分類建模。還開發了一種多類別ARKA方法，通過將其與定量跨越結構-活性關係（quantitative read-across structure-activity relationship, q-RASAR）框架整合，可能應用於基於回歸的問題。本書展示了活性懸崖概念的演變及其在QSAR預測中的當前狀態，並指出了活性懸崖研究的未來方向。藥物化學、預測毒理學、納米科學、食品科學、農業科學和材料資訊學領域的研究人員應能從活性懸崖的概念中受益，影響模型衍生的預測。

作者簡介

Dr. Kunal Roy Professor & Ex-Head in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013) and a former visiting scientist of Istituto di Ricerche Farmacologiche "Mario Negri" IRCCS, Milano. Italy. The field of his research interest is Quantitative Structure-Activity Relationship (QSAR) and Molecular Modeling with application in Drug Design, Property Modeling and Predictive Ecotoxicology. Dr. Roy has published more than 450 research articles in refereed journals (current SCOPUS h index 56; total citations to date more than 17000). He has also coauthored three QSAR-related books (Academic Press and Springer), edited thirteen QSAR books (Springer, Academic Press, and IGI Global), and published twenty five book chapters. Dr. Roy is the Co-Editor-in-Chief of Molecular Diversity (Springer Nature) and an Associate Editor of Computational and Structural Biotechnology Journal (Elsevier). Dr. Roy serves on the Editorial Boards of several International Journals including (1) European Journal of Medicinal Chemistry (Elsevier); (2) Journal of Molecular Graphics and Modelling (Elsevier); (3) Chemical Biology and Drug Design (Wiley); (4) Expert Opinion on Drug Discovery (Informa). Apart from this, Prof. Roy is a regular reviewer for QSAR papers in the journals like Chemosphere (Elsevier), Journal of Hazardous Materials (Elsevier), Ecotoxicology and Environmental Safety (Elsevier), Journal of Chemical Information and Modeling (ACS), ACS Omega (ACS), RSC Advances (RSC), Molecular Informatics (Wiley), SAR and QSAR in Environmental Research (T&F), etc. Prof. Roy has been recipient of several awards including AICTE Career Award (2003-04), DST Fast Track Scheme for Young Scientists (2005), Bioorganic and Medicinal Chemistry Most Cited Paper 2003-2006, 2004-2007 and 2006-2009 Awards from Elsevier, The Netherlands, Bioorganic and Medicinal Chemistry Letters Most Cited Paper 2006-2009 Award from Elsevier, The Netherlands, Professor R. D. Desai 80th Birthday Commemoration Medal & Prize (2017) from Indian Chemical Society, etc. Prof. Roy has been a participant in the EU funded projects nanoBRIDGES and IONTOX apart from several national Government funded projects (UGC, AICTE, CSIR, ICMR, DBT, DAE). Prof. Roy has recently been placed in the list of the World's Top 2% science-wide author database (whole career data) (World rank 52 in the subfield of Medicinal & Biomolecular Chemistry) (Ioannidis, John P.A. (2025), "August 2025 data-update for "Updated science-wide author databases of standardized citation indicators", Elsevier Data Repository).

Arkaprava Banerjee is a Researcher (funded by the Life Sciences Research Board, DRDO, Govt. of India) working at the Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata. Mr. Banerjee has twenty-nine research articles published in reputed journals and four book chapters with overall citations of 751 and an h-index of 17 (Scopus). His expertise lies in the similarity-based cheminformatic approaches like Read-Across and Read-Across Structure-Activity Relationship (RASAR) - a novel method that combines the concept of QSAR and Read-Across. Mr. Banerjee is also a Java programmer who has developed various cheminformatic tools based on QSAR, Read-Across, and RASAR, and the tools are freely available from the DTC Laboratory Supplementary Website. He received the Prof. Anupam Sengupta Bronze medal from Jadavpur University for securing the highest marks in Pharmaceutical Chemistry in the MPharm examination. He has also received a special diploma awarded by the Institute of Biomedical Chemistry, Moscow, Russia (2022), and the ASCCT Travel Award from the American Society for Cellular and Computational Toxicology (2023). Together with Prof. Kunal Roy, he has been one of the first researchers to develop quantitative models using similarity and error-based descriptors (quantitative/classification Read-Across Structure-Activity Relationship: q-RASAR/c-RASAR models) with applications in drug design, materials science, and property modeling. Recently, he coauthored a book on "q-RASAR," which was published by Springer. He has also co-edited three volumes of "Materials Informatics" published by Springer. He has recently been placed in the list of the World's Top 2% science-wide author database (Single-year data 2024) (World rank 769 in the subfield of Toxicology) (Ioannidis, John P.A. (2025), "August 2025 data-update for 'Updated science-wide author databases of standardized citation indicators', Elsevier Data Repository).

作者簡介(中文翻譯)

庫納爾·羅伊博士 印度加爾各答賈達普爾大學藥物技術系教授及前系主任。他曾獲得英聯邦學術人員獎學金（2007年，曼徹斯特大學）及瑪麗·居里國際來訪獎學金（2013年，曼徹斯特大學），並曾擔任意大利米蘭馬里奧·內格里藥理研究所（Istituto di Ricerche Farmacologiche 'Mario Negri' IRCCS）的訪問科學家。他的研究興趣領域為定量結構-活性關係（Quantitative Structure-Activity Relationship, QSAR）及分子建模，應用於藥物設計、性質建模及預測生態毒理學。羅伊博士在同行評審期刊上發表了超過450篇研究文章（目前SCOPUS h指數為56；至今總引用次數超過17000次）。他還共同撰寫了三本與QSAR相關的書籍（Academic Press和Springer），編輯了十三本QSAR書籍（Springer、Academic Press和IGI Global），並發表了二十五章書籍章節。羅伊博士是《分子多樣性》（Molecular Diversity，Springer Nature）的共同主編，並擔任《計算與結構生物技術期刊》（Computational and Structural Biotechnology Journal，Elsevier）的副編輯。羅伊博士還在多個國際期刊的編輯委員會中任職，包括（1）《歐洲藥物化學期刊》（European Journal of Medicinal Chemistry，Elsevier）；（2）《分子圖形與建模期刊》（Journal of Molecular Graphics and Modelling，Elsevier）；（3）《化學生物學與藥物設計》（Chemical Biology and Drug Design，Wiley）；（4）《藥物發現專家意見》（Expert Opinion on Drug Discovery，Informa）。此外，羅伊教授還是《Chemosphere》（Elsevier）、《危險材料期刊》（Journal of Hazardous Materials，Elsevier）、《生態毒理學與環境安全》（Ecotoxicology and Environmental Safety，Elsevier）、《化學信息與建模期刊》（Journal of Chemical Information and Modeling，ACS）、《ACS Omega》（ACS）、《RSC Advances》（RSC）、《分子信息學》（Molecular Informatics，Wiley）、《環境研究中的SAR與QSAR》（SAR and QSAR in Environmental Research，T&F）等期刊的QSAR論文常規審稿人。羅伊教授曾獲得多項獎項，包括AICTE職業獎（2003-04）、DST青年科學家快速通道計劃（2005）、Bioorganic and Medicinal Chemistry最被引用論文獎（2003-2006、2004-2007及2006-2009）來自荷蘭Elsevier、Bioorganic and Medicinal Chemistry Letters最被引用論文獎（2006-2009）來自荷蘭Elsevier、印度化學學會的R.D. Desai教授80週年紀念獎章及獎金（2017）等。羅伊教授參與了歐盟資助的nanoBRIDGES和IONTOX項目，以及多個國家政府資助的項目（UGC、AICTE、CSIR、ICMR、DBT、DAE）。羅伊教授最近被列入全球前2%科學作者數據庫（整個職業生涯數據）（在藥物與生物分子化學子領域的世界排名為52）（Ioannidis, John P.A. (2025), 'August 2025 data-update for 'Updated science-wide author databases of standardized citation indicators', Elsevier Data Repository）。

阿卡普拉瓦·班納吉（Arkaprava Banerjee）是印度政府國防研究與發展組織（DRDO）生命科學研究委員會資助的研究員，任職於加爾各答賈達普爾大學藥物技術系的藥物理論與化學信息學實驗室。班納吉先生在知名期刊上發表了二十九篇研究文章和四章書籍章節，總引用次數為751，h指數為17（Scopus）。他的專長在於基於相似性的化學信息學方法，如Read-Across和Read-Across結構-活性關係（RASAR）——這是一種結合QSAR和Read-Across概念的新方法。班納吉先生也是一名Java程序員，開發了多種基於QSAR、Read-Across和RASAR的化學信息學工具，這些工具可從DTC實驗室補充網站免費獲得。他因在藥物化學碩士考試中獲得最高分而獲得賈達普爾大學的安努帕姆·森古普塔教授銅獎。他還獲得了俄羅斯莫斯科生物醫學化學研究所頒發的特別文憑（2022年）以及美國細胞與計算毒理學會的ASCCT旅行獎（2023年）。與庫納爾·羅伊教授共同合作，他是首批使用相似性和基於誤差的描述符（定量/分類Read-Across結構-活性關係：q-RASAR/c-RASAR模型）開發定量模型的研究人員之一，應用於藥物設計、材料科學和性質建模。最近，他共同撰寫了一本關於「q-RASAR」的書籍，該書由Springer出版。他還共同編輯了三卷由Springer出版的《材料信息學》。他最近被列入全球前2%科學作者數據庫（單年數據2024）（在毒理學子領域的世界排名為769）（Ioannidis, John P.A. (2025), 'August 2025 data-update for 'Updated science-wide author databases of standardized citation indicators', Elsevier Data Repository）。