Pattern recognition system based on support vector machines: HIV-1 integrase inhibitors application

Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive Quantitative Structure-Activity Relationship (QSAR) models using molecular descriptors. The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship. Keywords: Support Vector Machines; Artificial Neural Network; Quantitative Structure-Activity Relationship

Application of support vector machines for prediction of anti-HIV activity of TIBO Derivatives.

The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship. Keywords: support vector machine (SVM); ANN; QSA

Investigations on Inhibitors of Hedgehog Signal Pathway: A Quantitative Structure-Activity Relationship Study

The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others

(Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review

There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored. (Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models

QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

Support vector machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR) models using molecular descriptors. Multiple linear regression (MLR) and artificial neural networks (ANNs) were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated

Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives

International audienc

Quantitative structure-activity relationship studies of TIBO derivatives using support vector machines

International audienc