q-RASAR [[electronic resource] ] : A Path to Predictive Cheminformatics / / by Kunal Roy, Arkaprava Banerjee

• Autore: Roy Kunal
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (99 pages)
• Disciplina: 542.85
• Soggetto topico: Chemistry - Data processing; Quantum physics; Computer simulation; Molecules - Models; Computational Chemistry; Quantum Simulations; Molecular Modelling
• Altri autori: BanerjeeArkaprava
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Q-RASAR
• ISBN: 3-031-52057-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910806198703321
• Lo trovi qui: Univ. Federico II
• Serie: SpringerBriefs in Molecular Science, . 2191-5415