A Computer-Based Approach to the Rational Discovery of New Trichomonacidal Drugs by Atom-Type Linear Indices

Authors: Marrero-Ponce, Yovani; Machado-Tugores, Yanetsy; Pereira, David M.; Escario, Jose A.; Barrio, Alicia G.; Nogal-Ruiz, Juan J.; Ochoa, Carmen; Aran, Vicente J.; Martinez-Fernandez, Antonio R.; Garcia Sanchez, Rory N.; Montero-Torres, Alina; Torrens, Francisco; Meneses-Marcel, Alfredo

Source: Current Drug Discovery Technologies, Volume 2, Number 4, December 2005 , pp. 245-265(21)

Publisher: Bentham Science Publishers

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Abstract:

Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic´ orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.

Keywords: TOMOCOMD-CARDD Software; Atom-Based Linear Index; LDA-Based QSAR Model; Trichomonacidal Activity; Virtual Screening; Lead Antitrichomonal Compound; Cytocidal activity; Heterocycles

Document Type: Research article

DOI: http://dx.doi.org/10.2174/157016305775202955

Affiliations: 1: Department of Pharmacy, Faculty of Chemistry-Pharmacy and Department of Drug Design, Chemical Bioactive Center. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba;

Publication date: 2005-12-01

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Due to the plethora of new approaches being used in modern drug discovery by the pharmaceutical industry, Current Drug Discovery Technologies has been established to provide comprehensive overviews of all the major modern techniques and technologies used in drug design and discovery. The journal is the forum for publishing both original research papers and reviews describing novel approaches and cutting edge technologies used in all stages of drug discovery. The journal addresses the multidimensional challenges of drug discovery science including integration issues of the drug discovery process.

Current Drug Discovery Technologies is an essential journal for all scientists and research managers involved in drug discovery who wish to keep abreast of all the modern techniques and technologies used in drug discovery.

A Computer-Based Approach to the Rational Discovery of New Trichomonacidal Drugs by Atom-Type Linear Indices

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