Quantitative Structure Activity Relationship screening

More recently (2006) we performed and reported quantitative structure-activity relationship (QSAR) modeling of the same compounds based on their atomic linear indices, for finding fimctions that discriminate between the tyrosinase inhibitor compounds and inactive ones [50]. Discriminant models have been applied and globally good classifications of 93.51 and 92.46% were observed for nonstochastic and stochastic hnear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67 and 89.44% [50]. In addition to this, these fitted models have also been employed in the screening of new cycloartane compounds isolated from herbal plants. Good behavior was observed between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds [50]. 

Dudek, A. Z., Arodz, T., Galvez, J. Gomputational methods in developing quantitative structure-activity relationships (QSAR) a review. Comb. Chem. High-Throughput Screen. 2006, 9, 213-228. 

T. (2006) A new strategy of high-speed screening and quantitative structure-activity relationship analysis to evaluate human ATP-binding cassette transporter ABCG2-drug interactions. Journal of Pharmacology... 

Key Words Biological activity cell-based partitioning chemical descriptors classification clustering distance-based design diversity selection high-throughput screening quantitative structure-activity relationship. 

In this chapter, we will give a brief introduction to the basic concepts of chemoinformatics and their relevance to chemical library design. In Section 2, we will describe chemical representation, molecular data, and molecular data mining in computer we will introduce some of the chemoinformatics concepts such as molecular descriptors, chemical space, dimension reduction, similarity and diversity and we will review the most useful methods and applications of chemoinformatics, the quantitative structure-activity relationship (QSAR), the quantitative structure-property relationship (QSPR), multiobjective optimization, and virtual screening. In Section 3, we will outline some of the elements of library design and connect chemoinformatics tools, such as molecular similarity, molecular diversity, and multiple objective optimizations, with designing optimal libraries. Finally, we will put library design into perspective in Section 4. 

Peterson, Y. K., Wang, X. S., Casey, P. J., Tropsha, A. (2009) Discovery of geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of quantitative structure-activity relationship modeling, virtual screening, and experimental validation. J Med Chem 52, 4210-4220. 

Tang, W.Z. and Pierotti, A.J., WWW database of disinfection by-product properties and related QSAR information, in Handbook on Quantitative Structure Activity Relationships (QSARs) for Pollution Prevention, Toxicity Screening, Risk Assessment and Web Applications, Walker, J.D., Ed, SETAC Press, Pensacola, FL, 2000. 

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