q-RASAR [electronic resource] : A Path to Predictive Cheminformatics /
Roy, Kunal.
q-RASAR A Path to Predictive Cheminformatics / [electronic resource] : by Kunal Roy, Arkaprava Banerjee. - 1st ed. 2024. - X, 91 p. 20 illus., 17 illus. in color. online resource. - SpringerBriefs in Molecular Science, 2191-5415 . - SpringerBriefs in Molecular Science, .
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.
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.
9783031520570
10.1007/978-3-031-52057-0 doi
--Data processing.
--Models.
QD39.3.E46
q-RASAR A Path to Predictive Cheminformatics / [electronic resource] : by Kunal Roy, Arkaprava Banerjee. - 1st ed. 2024. - X, 91 p. 20 illus., 17 illus. in color. online resource. - SpringerBriefs in Molecular Science, 2191-5415 . - SpringerBriefs in Molecular Science, .
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.
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.
9783031520570
10.1007/978-3-031-52057-0 doi
--Data processing.
--Models.
QD39.3.E46