TY  - BOOK
AU  - Roy,Kunal
AU  - Banerjee,Arkaprava
ED  - SpringerLink (Online service)
TI  - q-RASAR: A Path to Predictive Cheminformatics 
T2  - SpringerBriefs in Molecular Science,
SN  - 9783031520570
AV  - QD39.3.E46 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Chemistry
KW  - Data processing
KW  - Quantum physics
KW  - Computer simulation
KW  - Molecules
KW  - Models
KW  - Computational Chemistry
KW  - Quantum Simulations
KW  - Molecular Modelling
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-52057-0
ER  -