QSAR as a Random Event: Selecting of the Molecular Structure for Potential Anti-tuberculosis Agents

Title:QSAR as a Random Event: Selecting of the Molecular Structure for Potential Anti-tuberculosis Agents

Volume: 14 Issue: 1

Author(s): Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati and Roberto Fanelli

Affiliation:

Keywords: Anti-tuberculosis agent, Domain of applicability, Monte Carlo method, Validation, QSAR.

Abstract: Background: Molecular structure is the basis for establishing of quantitative structure –activity relationships (QSARs). The molecular graph as well as the simplified molecular input line entry systems (SMILES) are possible ways to represent the molecular structure for QSAR analysis by means of the CORAL software.

Methods: In spite of apparent influence of distribution of available data on the training set and validation set, the majority of works dedicated to quantitative structure - property/activity relationships (QSPRs/QSARs) are based solely on one split into visible set (i.e. the training set) and invisible set (i.e. the validation set). We deem that each QSAR approach should be estimated for a group of splits into the training set and validation set. The use of this principle for the case of antituberculosis agents is the essence of this work.

Results: Ten splits of the data on anti-tuberculosis agents into the training and test sets have been examined. The statistical approach to define the domain of applicability has been suggested and estimated. A collection of molecular structures which should be very effective anti-tuberculosis agents according to the established model is suggested.

Conclusion: The CORAL software available on the Internet can be used for the QSAR analysis of other molecular structures which are capable anti-infective agents.

Cite this article as:

A. Toropov Andrey, Toropova P. Alla, Benfenati Emilio and Fanelli Roberto, QSAR as a Random Event: Selecting of the Molecular Structure for Potential Anti-tuberculosis Agents, Anti-Infective Agents 2016; 14 (1) . https://dx.doi.org/10.2174/221135251401160302120554