Abstract
A novel approach (TOMOCOMD-CARDD) to computer-aided “rational” drug design is illustrated. This approach is based on the calculation of the non-stochastic and stochastic linear indices of the molecular pseudograph’s atom-adjacency matrix representing molecular structures. These TOMOCOMD-CARDD descriptors are introduced for the computational (virtual) screening and “rational” selection of new lead antibacterial agents using linear discrimination analysis. The two structure-based antibacterial-activity classification models, including non-stochastic and stochastic indices, classify correctly 91.61% and 90.75%, respectively, of 1525 chemicals in training sets. These models show high Matthews correlation coefficients (MCC=0.84 and 0.82). An external validation process was carried out to assess the robustness and predictive power of the model obtained. These QSAR models permit the correct classification of 91.49% and 89.31% of 505 compounds in an external test set, yielding MCCs of 0.84 and 0.79, respectively. The TOMOCOMD-CARDD approach compares satisfactorily with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, an in silico screening of 87 new chemicals reported in the anti-infective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new lead antibacterial compounds.
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Acknowledgments
The authors thank both referees for their critical opinions about the manuscript, which have significantly contributed to improving its presentation and quality. F.T. acknowledges financial support from the Spanish MCT (Plan Nacional I+D+I, Project No. BQU2001-2935-C02-01) and Generalitat Valenciana (DGEUI INF01-051 and INFRA03-047, and OCYT GRUPOS03-173). Finally, Marrero-Ponce is also indebted to the editorial assistant, Isabelle Bundesmann for her kind attention. One of the authors (M-P. Y) thanks the program ‘Estades Temporals per a Investigadors Convidats’ for a fellowship to work at Valencia University.
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Marrero-Ponce, Y., Marrero, R.M., Torrens, F. et al. Non-stochastic and stochastic linear indices of the molecular pseudograph’s atom-adjacency matrix: a novel approach for computational in silico screening and “rational” selection of new lead antibacterial agents. J Mol Model 12, 255–271 (2006). https://doi.org/10.1007/s00894-005-0024-8
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DOI: https://doi.org/10.1007/s00894-005-0024-8