Quantitative structure-activity descriptor pharmacophore

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... 

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. 

These pharmacophore techniques are different in format from the traditional pharmacophore definitions. They can not be easily visualized and mapped to the molecular structures rather, they are encoded as keys or topological/topographical descriptors. Nonetheless, they capture the same idea as the classic pharmacophore concept. Furthermore, this formalism is quite useful in building quantitative predictive models that can be used to classify and predict biological activities. 

The quantitative comparison of the optimized 3D structure of a selected set of ligands allows the development of their minimal 3D structural requirements for the recognition and activation of the biological target, that is, the pharmacophore hypothesis, and gives a sound 3D rationale to the available SARs [21]. A more complete and mechanistically relevant approach to the development of the 3D pharmacophore consists in its translation into a numerical molecular descriptor that quantifies the molecular-pharmacophore similarity-diversity for computational QSAR modeling [21,41]. 

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