Banerjee Arkaprava (81 results)
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Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
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Condition: New. 1st ed. 2024 edition NO-PA16APR2015-KAP.
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- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 101 pages. 9.25x6.10x0.22 inches. In Stock.
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Language: English
Published by Springer Nature Switzerland, Springer Nature Switzerland, 2024
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) a…nd read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-RASAR models demonstrating practical examples. It also showcases successful case studies on the application of q-RASAR modeling in medicinal chemistry, predictive toxicology, and materials sciences. The book also includes the tools used for q-RASAR model development for new users. It is a valuable resource for researchers and students interested in grasping the development algorithm of q-RASAR models and their application within specific research domains.
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- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 th…e 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.
- Softcover
Seller: Buchpark, Trebbin, GermanyBuchpark
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£ 19.58
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Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships…(QSAR) and read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-RASAR models demonstrating practical examples. It also showcases successful case studies on the application of q-RASAR modeling in medicinal chemistry, predictive toxicology, and materials sciences. The book also includes the tools used for q-RASAR model development for new users. It is a valuable resource for researchers and students interested in grasping the development algorithm of q-RASAR models and their application within specific research domains.
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Language: English
Published by Elsevier - Health Sciences Division, Philadelphia, 2026
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment (Advances in Green and Sustainable Chemistry)
Roy PhD FRSC, Kunal (Editor)/ Banerjee MRSC, Arkaprava (Editor)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 350 pages. In Stock.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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- Softcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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- Softcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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Condition: New. 2026. paperback. . . . . .
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: moluna, Greven, Germanymoluna
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£ 180.79
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Condition: New. Presents multiple algorithms for QSPR models and machine learning methods for modeling environmental endpointsDiscusses crucial emerging topics in sustainable chemistry, such as mixture property modeling, microplastic toxicity modeling, and.
Language: English
Published by Elsevier - Health Sciences Division, Philadelphia, 2026
- Softcover
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Materials Informatics III | Polymers, Solvents and Energetic Materials | Kunal Roy (u. a.) | Taschenbuch | Challenges and Advances in Computational Chemistry and Physics | xv | Englisch | 2026 | Springer | EAN 9783031787263 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17,… 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Materials Informatics II | Software Tools and Databases | Kunal Roy (u. a.) | Taschenbuch | Challenges and Advances in Computational Chemistry and Physics | xvi | Englisch | 2026 | Springer | EAN 9783031787300 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelb…erg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Language: English
Published by Springer International Publishing AG, Cham, 2025
- Hardcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Hardcover. Condition: new. Hardcover. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep e…utectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structureproperty relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment (Advances in Green and Sustainable Chemistry)
Roy PhD FRSC, Kunal (Editor)/ Banerjee MRSC, Arkaprava (Editor)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 350 pages. 5.81x2.11x9.01 inches. In Stock.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics. It covers a range of topics, including electronic properties of metal nanoclust…ers, carbon quantum dots, toxicity assessments of nanomaterials, and predictive modeling for fullerenes and perovskite materials. Additionally, the book discusses multiscale modeling and advanced decision support systems for nanomaterial risk management, while also highlighting various machine learning tools, databases, and web platforms designed to predict the properties of materials and molecules. It is a comprehensive guide and a great tool for researchers working at the intersection of machine learning and material sciences.
- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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