A Path to Predictive Cheminformatics Roy, Kunal. creator author. aut http://id.loc.gov/vocabulary/relators/aut Banerjee, Arkaprava. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access X, 91 p. 20 illus., 17 illus. in color. online resource. 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. Chemical Information and Molecular Similarity -- Read-across and Quantitative Structure-activity Relationships (QSAR) for Making Predictions and Data Gap-Filling -- Quantitative Read-Across (q-RA) and Quantitative Read-Across Structure-Activity Relationships (q-RASAR) – Genesis and Model Development -- Tools, Applications, and Case Studies (q-RA and q-RASAR) -- Future Prospects. by Kunal Roy, Arkaprava Banerjee. Chemistry Data processing Quantum physics Computer simulation Molecules Models Computational Chemistry Quantum Simulations Molecular Modelling QD39.3.E46 9783031520570 https://doi.org/10.1007/978-3-031-52057-0 https://doi.org/10.1007/978-3-031-52057-0 240125 20251030111402.0 978-3-031-52057-0