Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R(2 )= 0.664, F = 13.588, and R(cv)(2) (LOO) = 0.542 while it achieves R(2 )= 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The R(pred)(2) is 0.777, reflecting a relatively good predictive power for the external set.
This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: In this publication, QSAR models were developed to predict analgesic and anti-inflammatory activities of some 2-benzoxazolinone derivatives using multiple linear regression method. The models were validated internally and externally according to the OECD principles. With the help of these models, pronounced molecular properties of these compounds related to activities were also explored. The developed models demonstrated that hydrophobicity, the number of halogens, and the shape of the molecular structure of these candidate drugs are prominent to represent analgesic and anti-inflammatory activities. Based on the previously tested compounds and the developed models, 77 new compounds were designed as potential analgesic and anti-inflammatory drugs. Majority of the newly designed compounds demonstrated promising analgesic and anti-inflammatory activity.(7 of 9) 1800090 Figure 4. Insubria graph. Predicted vs. leverage values of (a) A activity model and (b) AI activity model.
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