Roy, Kunal. <br/><br/>
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, .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783031520570 
<br/><br/><label>Standard No.: </label>10.1007/978-3-031-52057-0  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>--Data processing.<br/><br/><br/>--Models.<br/><br/><br/>
<br/><br/><label>LC Class. No.: </label>QD39.3.E46