q-RASAR / SpringerBriefs in Molecular Science (PDF)
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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.
Mr. Arkaprava Banerjee is a Researcher (funded by the Life Sciences Research Board, DRDO, Govt. of India) working at the Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata. Mr. Banerjee has thirteen research articles published in reputed journals and one book chapter with overall citations of 127 and an h-index of 6 (Scopus). His ORCID identifier is 0000-0001-8468-0784. His expertise lies in similarity-based cheminformatic approaches like Read-Across and Read-Across Structure-Activity Relationship (RASAR), a novel method that combines the concepts of QSAR and Read-Across. Mr. Banerjee is also a Java programmer, who has developed various cheminformatic tools based on QSAR, Read-Across, and RASAR, and the tools are freely available from the DTC Laboratory Supplementary Website. He has received the Prof. Anupam Sengupta Bronze medal from Jadavpur University for securing the highest marks in Pharmaceutical Chemistry in the MPharm Examination. He has also received a special diploma awarded by the Institute of Biomedical Chemistry, Moscow, Russia. Mr. Banerjee received a Travel award from the American Society for Cellular and Computational Toxicology (ASCCT) to make an Oral Presentation at the QSAR2023 conference in Copenhagen, Denmark. Together with Prof. Kunal Roy, he has been one of the first researchers to develop quantitative models using similarity and error-based descriptors (quantitative/classification Read-Across Structure-Activity Relationship: q-RASAR/c-RASAR models) with applications in drug design, materials science, and property modeling.
- Autoren: Kunal Roy , Arkaprava Banerjee
- 2024, 1st ed. 2024, 91 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3031520572
- ISBN-13: 9783031520570
- Erscheinungsdatum: 25.01.2024
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- Größe: 2.31 MB
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