Quantitative structure-activity relationships 3-D QSAR

While 3-D pharmacophore/agrophore searches indicate best matches of relevant key functions, i.e., (semi)qualitative result, a linear regression derived from a statistical analysis of spatial features and measured activities yields quantitative structure-activity relationships (3-D QSAR). This may be extremely helpful for a detailed interpretation of existing results and the activity prediction of new or hypothetical compounds (Figure 3). 

A CoMFA study was conducted on a series of fused thiadiazine derivatives (86 and 87) with PDF 7 inhibitory activity in order to determine alternative molecular regions that could be modified to improve both activity on PDE 7 and selectivity versus PDE 4 and PDE 3. The main conclusion of this three-dimensional quantitative structure-activity relationship (3-D QSAR) study revealed the importance of hydrogen bond interactions for phosphodiesterase activity <2001EJM333, 2000JMC3218>. This is consistent with the lack of activity shown by disubstituted compounds 88 <2000JMC683>. 

Quantitative Structure-Activity Relationship models are used increasingly in chemical data mining and combinatorial library design [5, 6]. For example, three-dimensional (3-D) stereoelectronic pharmacophore based on QSAR modeling was used recently to search the National Cancer Institute Repository of Small Molecules [7] to find new leads for inhibiting HIV type 1 reverse transcriptase at the nonnucleoside binding site [8]. A descriptor pharmacophore concept was introduced by us recently [9] on the basis of variable selection QSAR the descriptor pharmacophore is defined as a subset of... 

Propafenone 143, a drug in clinical use as an antiarrhythmic, has activity in the modulation of cancer multidrug resistance. A series of benzofuran analogues of propafenone, such as compound 144, have been synthesized and evaluated in a daunomycin cytotoxicity assay <1996JME4767>. The results of this work were later the subject of a comparative molecular field analysis (3-D quantitative structure-activity relationship (QSAR)) <1998QSA301>. 

Blin, N., Federici, C, Koscielniak, T. and Strosberg, A.D. (1995). Predictive Quantitative Structure-Activity Relationships (QSAR) Analysis of Beta 3-Adrenergic Ligands. Drug Design Discovery, 12,297-311. 

Hansch C, Hoekman D, Leo A, Zhang LT, Li P. The expanding role of quantitative structure-activity relationships (QSAR) in toxicology. Toxicol Lett 1995 79(l-3) 45-53. 

Winkler, D. A. 2002. The role of quantitative structure—activity relationships (QSAR) in biomolecular discovery. Brief Bioinform 3 73-86. 

Quantitative structure-activity relationships QSAR. The QSAR approach pioneered by Hansch and co-workers relates biological data of congeneric structures to physical properties such as hydrophobicity, electronic, and steric effects using linear regression techniques to estimate the relative importance of each of those effects contributing to the biological effect. The molecular descriptors used can be 1-D or 3-D (3D-QSAR). A statistically sound QSAR regression equation can be used for lead optimization. 

Peny, N.C, Davies, E.K. The Use of 3-D Modelling Databases for Identifying Structure Activity Relationships . In QSAR Quantitative Structure-Activity Relationships in Drug Design, Fauchere, J.L., Ed. Alan R. Liss New York, 1989, pp.189-193. 

Quantitative structure-activity or structure-performance (property) relationships (QSAR and QSPR respectively) are of increasing interest in a wide diversity of technology areas. With some notable exceptions, most QSA(P)R studies associated with heterogeneous catalysis are restricted to zeolite-based catalysts. The reasoning is simple -the well defined 3-D structures, of molecular-sieve zeolites, are a reasonable representation of the catalyst "surface", hence bulk characterisation provides information on the catalyst. It is for a similar reason that the majority of modelling studies involves zeolites and zeotypes. 

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