Arkaprava Banerjee – författare
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7 produkter
7 produkter
- Nyhet
Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment
Häftad, Engelska, 2026
2 136 kr
Skickas inom 11-20 vardagar
Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment covers the theory and practices of chemical informatics, focusing on modeling various properties and endpoints related to chemicals for improved chemical management and the design of safer chemicals to promote environmental sustainability. Across four sections, the book outlines modeling techniques such as quantitative structure-property relationship (QSPR), read-across, and machine learning for modeling environmental endpoints of chemicals. OECD guidelines are discussed and considered for model development and validation, documentation using the QSAR modeling reporting format (QMRF), and regulatory requirements for result presentation.The book offers full datasets, algorithm information, and real-world case studies for all models, along with worked examples. It will serve as an essential resource for chemists and environmental scientists working in green and sustainable chemistry, but will be a great resource for students and academics at graduate level and above studying cheminformatics. This book will also be of interest to researchers developing new and sustainable chemicals and for decision-makers looking to make industrial processes more sustainable.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 natural language models for toxicity and ecotoxicity predictionProvides a comprehensive framework for modeling physicochemical properties, environmental thresholds, and acute and chronic toxicity endpointsIncludes more than 20 real-world case studies, featuring datasets for environmental endpoints, with examples of model development and methodology
3 220 kr
Kommande
Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipeline—from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drug–target interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development.
540 kr
Skickas inom 10-15 vardagar
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.
Del 41 - Challenges and Advances in Computational Chemistry and Physics
Materials Informatics III
Polymers, Solvents and Energetic Materials
Inbunden, Engelska, 2025
2 345 kr
Skickas inom 5-8 vardagar
This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials.
Del 40 - Challenges and Advances in Computational Chemistry and Physics
Materials Informatics II
Software Tools and Databases
Inbunden, Engelska, 2025
2 357 kr
Skickas inom 10-15 vardagar
This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics.
Del 39 - Challenges and Advances in Computational Chemistry and Physics
Materials Informatics I
Methods
Inbunden, Engelska, 2025
2 997 kr
Skickas inom 10-15 vardagar
This contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques.
540 kr
Skickas inom 10-15 vardagar
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.