Prediction of bioactivity of HIV-1 integrase ST inhibitors by multilinear regression analysis and support vector machine

Xuan, Shouyi; Wu, Yunshan; Chen, Xiaofang; Li, Jun; Yan, Aixia

doi:10.1016/j.bmcl.2013.01.081

Cited by 15 publications

(7 citation statements)

References 64 publications

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“…SVM, which have been employed in QSAR studies , is another approach utilized to model the relation between descriptors and activity in this study. SVM constructs a hyperplane in a multidimensional space to map the PCs related to the activity onto a higher dimensional feature space by kernel function .…”

Section: Methodsmentioning

confidence: 99%

Image‐Based Analysis to Predict the Activity of Tariquidar Analogs as P‐Glycoprotein Inhibitors: The Importance of External Validation

Shayanfar

Ghandadi

2015

Archiv der Pharmazie

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Permeability glycoprotein (P-gp) is involved in the pathology of various diseases including cancer and epilepsy, mainly through the translocation of some medicines across the cell membrane. Here, we employed image-based quantitative structure-activity relationship (QSAR) models to predict the P-gp inhibitory activity of some Tariquidar derivatives. The structures of 65 Tariquidar derivatives and their P-gp inhibition activities were collected from the literature. For each compound, the pixels of bidimensional images and their principal components (PCs) were calculated using MATLAB software. Various statistical methods including principal component regression, artificial neural networks, and support vector machines were employed to investigate the correlation between the PCs and the activity of the compounds. The predictability of the models was investigated using external validation and applicability domain analysis. An artificial neural network-based model demonstrated the best prediction results for the test set. Moreover, external validation analysis of the developed models supports the idea that R(2) cannot assure the validity of QSAR models and another criterion, i.e., the concordance correlation coefficient (CCC) parameter, should be involved to evaluate the validity of the QSAR models. The results of this study indicate that image analysis could be as suitable as descriptors calculated by commercial software to predict the activity of drug-like molecules.

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Section: Methodsmentioning

confidence: 99%

Image‐Based Analysis to Predict the Activity of Tariquidar Analogs as P‐Glycoprotein Inhibitors: The Importance of External Validation

Shayanfar

Ghandadi

2015

Archiv der Pharmazie

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“…Ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors of HIV-1 (Pancholi et al 2014) has been done India respectively. Prediction of bioactivities of HIV-1 integrase ST inhibitors (Xuan et al 2013) and classifi cation of active and weakly active ST inhibitors of HIV-1 integrase has (Yan et al 2012) been done using machine learning approaches in China and USA respectively. Prediction of interactions between HIV-1 and human proteins using SVM in China has also done (Wei et al 2015).…”

Section: The Hiv Enzymes Rolementioning

confidence: 99%

Machine learning approaches to study HIV/AIDS infection: A Review

Kumari¹,

Chouhan²,

Suryawanshi³

2017

Biosci. Biotech. Res. Comm

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“…These molecules interfere with either the 3′ processing step, the ST step, or both. Inhibitors of the ST step tend to exhibit better inhibitory activity, and we have reported several classification and QSAR models for ST inhibitors 12,13. However, 3′P inhibitors of HIV‐1 integrase can also exhibit high inhibitory activity.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV‐1 Integrase

Xuan

Wang

Kang

et al. 2013

Molecular Informatics

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Inhibition of the 3' processing step of HIV-1 integrase by small molecule inhibitors is one of the most promising strategies for the treatment of AIDS. Using a support vector machine (SVM) approach, we developed six classification models for predicting 3'P inhibitors. The models are based on up to 48 selected molecular descriptors and a comprehensive data set of 1253 molecules, with measured activities ranging from nanomolar to micromolar IC50 values. Model B2, the most robust SVM model, obtains a prediction accuracy, sensitivity, specificity and Matthews correlation coefficient (MCC) of 93 %, 81 %, 94 % and 0.67 on the test set, respectively. The presence of hydrogen bonding features and hydrophilicity in general were identified as key determinants of inhibitory activity. Further important properties include molecular refractivity, π atom charge, total charge, lone pair electronegativity, and effective atom polarizability. Comparative fragment-based analysis of the active and inactive molecules corroborated these observations and revealed several characteristic structural elements of 3'P inhibitors. The models built in this study can be obtained from the authors.

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Prediction of bioactivity of HIV-1 integrase ST inhibitors by multilinear regression analysis and support vector machine

Cited by 15 publications

References 64 publications

Image‐Based Analysis to Predict the Activity of Tariquidar Analogs as P‐Glycoprotein Inhibitors: The Importance of External Validation

Image‐Based Analysis to Predict the Activity of Tariquidar Analogs as P‐Glycoprotein Inhibitors: The Importance of External Validation

Machine learning approaches to study HIV/AIDS infection: A Review

Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV‐1 Integrase

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