3D quantitative structure-activity relationships

Zhao IS, Wang B, Dai ZX, Wang XD, Kong LR, Wang LS. 3D-quantitative structure-activity relationship stndy of organophosphate compounds. Chinese Sci Bull 2004 49 240-5. 

Sippl, W. Receptor-based 3D Quantitative Structure-Activity Relationships of Estrogen Receptor Ligands./. Comput.-Aided. Mol. Des., 2000, 14, 559-572. 

Miigge, I., Podlogary, B.L. 3D Quantitative Structure-Activity Relationships of Biphenyl Carboxylic Acid MMP-3 Inhibitors Exploring Automated Docking as Alignment Method. Quant. Struct.-Act. Relat. 2001, 20, 215-223. 

Kim, K.H. (1992a). 3D Quantitative Structure-Activity Relationships Description of Electronic Effects Directly from 3D Structures Using a Grid Comparative Molecular Field Analysis (CoMFA) Approach. QuantStruct-ActRelat, 11,127-134. 

Kroemer, R.T., Ettmayer, P. and Hecht, P. (1995). 3D-Quantitative Structure-Activity Relationships of Human Immunodeficiency Virus Type-1 Proteinase Inhibitors Comparative Molecular Field Analysis of 2-Heterosubstituted Statine Derivatives - Implications for the Design of Novel Inhibitors. 7.Med.Chein., 38,4917-4928. 

Masuda, T., Nakamura, K., Jikihara, T., Kasuya, E, Igarashi, K., Fukui, M., Takagi, T. and Fuji-wara, H. (1996). 3D Quantitative Structure-Activity Relationships for Hydrophobic Interactions. Comparative Molecular Field Analysis (CoMFA) Including Molecular Lipophilicity Potentials as Applied to the Glycine Conjugation of Aromatic as well as Aliphatic Carboxylic Acids. Quant.Struct.-Act.Relat., 15,194-200. 

Thull, U., Kneubuhler, S., Gaillard, R, Carrupt, P.-A., Testa, B., Altomare, C, Carotti, A., Jenner, P. and McNaught, K.S. (1995). Inhibition of Monoamine Oxidase by Isoquinoline Derivatives Qualitative and 3D Quantitative Structure-Activity Relationships. Biochem.Pharmacol., 50, 869-877. 

Tominaga, Y. and Fujiwara, I. (1997b). Novel 3D Descriptors Using Excluded Volume Application to 3D Quantitative Structure-Activity Relationships. J.Chem.Inf.Comput.ScL, 37, 1158-1161. 

Ivanciuc, O., Ivanciuc, T. and Cabrol-Bass, D. (2000b) 3D quantitative structure-activity relationships with CoRSA. Comparative receptor surface analysis. Application to calcium channel agonists. Analusis, 28, 637—642. 

Kim, KH. (1992) 3D quantitative structure-activity relationships description of electronic effects directly from 3D structures using a grid comparative molecular field analysis (CoMFA) approach. Quant. Struct. -Act. Rdat., 11, 127-134. 

Kim, K.H. (1993a) 3D quantitative structure-activity relationships describing hydrophobic interactions... 

Stiefl, N. and Baumann, K. (2003) Mapping property distributions of molecular surfaces algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique. J. Med. Chem.,... 

Woolfrey, J.R., Avery, M.A. and Doweyko, A.M. (1998) Comparison of 3D quantitative structure-activity relationship methods analysis of the in vitro antimalarial activity of 154 artemisinin analogues by hypothetical active-site lattice and comparative molecular field analysis./. Comput. Aid. Mol. Des., 12, 165-181. 

Muegge I, Podlogar B. 3D-quantitative structure activity relationship of biphenyl carboxylic acid MMP-3 inhibitors exploring automated docking as alignment tool. Quant Struct-Act Relat 2001 20 215-222. 

Formulation of an alternative scheme for 3D quantitative structure-activity relationship (3D-QSAR) modeling... 

K. H. Kim, Med. Chem. Res., 2, 22 (1992). Description of Nonlinear Dependence Directly from 3D Structures in 3D-Quantitative Structure-Activity Relationships. 

Kroemer, R. T., Ettmayer, R, and Hecht, R, 3D-quantitative structure-activity relationships of human immunodeficiency virus type-1 proteinase inhibitors Comparative molecular field analysis of 2-heterosubstituted statine derivatives-implications for the design of novel inhibitors. /. Med. Chem. 1995, 38 (25), 4917-4928. 

Researchers have used 3D quantitative structural activity relationship (QSAR) of deet and related analogs to construct pharmacophores to better understand the structural basis that leads to repellency by these amide compounds."- Their model was constructed primarily from the protection time data of Suryanarayana and others. Ma and others" showed that one could predict repellent duration based on compound structure and specifically that the amide group and attached substituents played a significant role in the experimentally determined repellent efficacy. Using the same data set, Katritzky and others applied Codessa Pro software to develop a QSAR model for the prediction of complete protection time (CPT) from descriptors related to the structural and electronic properties of deet analogs. This work is the foundation for current projects that involve the examination of repellency and toxicity data for subsets of compounds within the U.S. Department of Agriculture (USDA) archive. 

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