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M. Freitas, Steven Brown, J. Martins (2005)
MIA-QSAR: a simple 2D image-based approach for quantitative structure–activity relationship analysisJournal of Molecular Structure, 738
Hua Gao, C. Williams, P. Labute, J. Bajorath (1999)
Binary Quantitative Structure-Activity Relationship (QSAR) Analysis of Estrogen Receptor LigandsJournal of chemical information and computer sciences, 39 1
S. Ajmani, Kamalakar Jadhav, Sudhir Kulkarni (2009)
Group‐Based QSAR (G‐QSAR): Mitigating Interpretation Challenges in QSARQsar & Combinatorial Science, 28
Manoj Damale, S. Harke, Firoz Khan, D. Shinde, J. Sangshetti (2014)
Recent advances in multidimensional QSAR (4D-6D): a critical review.Mini reviews in medicinal chemistry, 14 1
M. Doddareddy, Yeon Lee, Y. Cho, K. Choi, H. Koh, A. Pae (2004)
Hologram quantitative structure activity relationship studies on 5-HT6 antagonists.Bioorganic & medicinal chemistry, 12 14
T. Naumann, David Lowis (1997)
HQSAR: A New, Highly Predictive QSAR Technique
A. Cherkasov, E. Muratov, D. Fourches, A. Varnek, I. Baskin, M. Cronin, J. Dearden, P. Gramatica, Y. Martin, R. Todeschini, V. Consonni, V. Kuz'min, R. Cramer, R. Benigni, Chihae Yang, J. Rathman, L. Terfloth, J. Gasteiger, A. Richard, A. Tropsha (2014)
QSAR modeling: where have you been? Where are you going to?Journal of medicinal chemistry, 57 12
[The QSAR/QSPR technique is now a widely practiced tool in chemical research both in the industry and academia. Because of the enormous potential applications of predictive modeling analysis, various newer methods have recently been developed to improve the usefulness and applicability of QSAR techniques. Binary QSAR, hologram QSAR (HQSAR), group-based QSAR (G-QSAR), multivariate image analysis (MIA)-based QSAR (MIA-QSAR), etc., are some of the new approaches in the realm of QSAR formalisms. Furthermore, QSAR techniques are also employed in various newer research areas in addition to the conventional drug design and predictive toxicology paradigm. QSAR models have been observed to be fruitful in modeling various property endpoints in the field of material informatics. In addition to that, predictive modeling of properties and/or toxicities of nanoparticles (NPs), cosmetics, peptides, ionic liquids, phytochemicals, etc., also represents the emerging application areas of the QSAR technique. This present chapter gives an overview of both the new methods and new application areas of QSAR studies.]
Published: Apr 12, 2015
Keywords: Binary QSAR; Cosmetics; G-QSAR; HQSAR; Inverse QSAR; MIA-QSAR; Mixture toxicity; Nanomaterials; Peptides; Phytochemistry
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