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首页> 外文期刊>Journal of the Brazilian Chemical Society >In silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach
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In silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

机译：基于TOMOCOMD-CARDD方法的计算机硅抗菌活性建模

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In the recent times, the race to cope with the increasing multidrug resistance of pathogenic bacteria has lost much of its momentum and health professionals are grasping for solutions to deal with the unprecedented resistance levels. As a result, there is an urgent need for a concerted effort towards the development of new antimicrobial drugs to stay ahead in the fight against the ever adapting bacteria. In the present report, antibacterial classification functions (models) based on the topological molecular computational design-computer aided ‘‘rational’’ drug design (TOMOCOMD-CARDD) atom-based non-stochastic and stochastic bilinear indices are presented. These models were built using the linear discriminant analysis (LDA) method over a balanced chemical compounds dataset of 2230 molecular structures, with a diverse range of structural and molecular mechanism modes. The results of this study indicated that the non-stochastic and stochastic bilinear indices provided excellent classification of the chemical compounds (with accuracies of 86.31% and 84.92%, respectively, in the training set). These models were further externally validated yielding correct classification percentages of 86.55% and 87.91% for the non-stochastic and stochastic bilinear models, respectively. Additionally, the obtained models were compared with those reported in the literature and demonstrated comparable results, although the latter were built over much smaller datasets and with much higher degrees of freedom. Finally, simulated ligand-based virtual screening of 116 compounds, recently identified as potential antibacterials, was performed yielding 86.21% and 83.62% of correct classification, respectively, and thus demonstrating the utility of the obtained TOMOCOMD-CARDD models in the search of novel compounds with desirable antibacterial activity.
机译：近年来，应对病原菌日益增长的多药耐药性的竞赛已失去了很多动力，卫生专业人员正在寻求解决方案以应对前所未有的耐药性。结果，迫切需要共同努力以开发新的抗微生物药物，以在对抗不断变化的细菌方面保持领先地位。在本报告中，提出了基于拓扑分子计算设计-计算机辅助“理性”药物设计（TOMOCOMD-CARDD）的基于原子的非随机和随机双线性指标的抗菌分类功能（模型）。这些模型是使用线性判别分析（LDA）方法在2230个分子结构的平衡化合物数据集中建立的，具有各种结构和分子机理模式。这项研究的结果表明，非随机和随机双线性指数对化合物进行了出色的分类（在训练集中的准确度分别为86.31％和84.92％）。对这些模型进行了进一步的外部验证，得出非随机和随机双线性模型的正确分类百分比分别为86.55％和87.91％。此外，尽管将后者建立在更小的数据集和更高的自由度上，但将获得的模型与文献报道的模型进行了比较，并证明了可比的结果。最后，对最近被鉴定为潜在抗菌药的116种化合物进行了基于配体的模拟虚拟筛选，分别产生了正确分类的86.21％和83.62％，从而证明了获得的TOMOCOMD-CARDD模型在寻找新化合物中的实用性具有理想的抗菌活性。

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