期刊
SAR AND QSAR IN ENVIRONMENTAL RESEARCH
卷 28, 期 12, 页码 1011-1023出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/1062936X.2017.1397056
关键词
QSAR; rational partition of dataset; k-means; Kennard-Stone; based on activity; random selection
类别
资金
- Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) [PIP 11220130100311]
- Facultad de Quimica, Bioquimica y Farmacia, Universidad Nacional de San Luis (UNSL) [PROICO 2-1514]
This study performed an analysis of the influence of the training and test set rational selection on the quality and predictively of the quantitative structure-activity relationship (QSAR) model. The study was carried out on three different datasets of Influenza Neuraminidase (H1N1) inhibitors. The three datasets were divided into training and test sets using three rational selection methods: based on k-means, Kennard-Stone algorithm and Activity and the results were compared with Random selection. Then, a total of 31,490 mathematical models were developed and those models that presented a determination coefficient higher than: r(train)(2) > 0.8, r(loo)(2) > 0.7, r(test)(2) > 0.5 and minimum standard deviation (SD) and minimum root-mean square error (RMS) were selected. The selected models were validated using the internal leave-one-out method and the predictive capacity was evaluated by the external test set. The results indicate that random selection could lead to erroneous results. In return, a rational selection allows for obtaining more reliable conclusions. The QSAR models with major predictive power were found using the k-means algorithm and selection by activity.
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