An automated workflow by using KNIME Analytical Platform: a case study for modelling and predicting HIV-1 protease inhibitors (original) (raw)
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The prediction of biological activity of a chemical compound from its structural features plays an important role in drug design. In this paper, we discuss the quantitative structure activity relationship (QSAR) prediction models developed on a dataset of 170 HIV protease enzyme inhibitors. Various chemical descriptors that encode hydrophobic, topological, geometrical and electronic properties are calculated to represent the structures of the molecules in the dataset. We use the hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction. The linear multiple regression analysis (MLR), correlation-based feature selection (CFS), non-linear decision tree (DT), and artificial neural network (ANN) approaches are used as fitness functions. The selected descriptors represent the overall descriptor space and account well for the binding nature of the considered dataset. These selected features are also human interpretable and can be used to explain the interactions between a drug molecule and its receptor protein (HIV protease). The selected descriptors are then used for developing the QSAR prediction models by using the MLR, DT and ANN approaches. These models are discussed, analyzed and compared to validate and test their performance for this dataset. All three approaches yield the QSAR models with good prediction performance. The models developed by DT and ANN are comparable and have better prediction than the MLR model. For ANN model, weight analysis is carried out to analyze the role of various descriptors in activity prediction. All the prediction models point towards the involvement of hydrophobic interactions. These models can be useful for predicting the biological activity of new untested HIV protease inhibitors and virtual screening for identifying new lead compounds.
2011 International Conference on Multimedia Computing and Systems, 2011
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European Journal of Medicinal Chemistry, 2010
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The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naïve or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is lifelong , the emergence of HIV-1 strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r 2 = 0.998, q 10CV 2 = 0.721, q external_test 2 = 0.754) and a boosted K* algorithm (r 2 = 0.987, q 10CV 2 = 0.721, q external_test 2 = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top ranked compounds were further evaluated for their target engagement activity using molecular docking studies and their potential as INSTIs evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.
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Additional file 1. Supporting information including Table S1. Performance of QSAR predictive models on three times randomly picked ~ 10% independent/validation data. These models were developed using remaining ~ 90% data during training/testing respectively for each of the six datasets; Table S2. Details of statistical parameters used for the development of IC50 based QSAR models; Table S3. Details of statistical parameters used for the development of percent inhibition based QSAR models; Table S4. Details of chemical descriptors used in the development of IC50 based QSAR models; Table S5. Details of chemical descriptors used in the development of percent inhibition based QSAR models; Table S6. Details of slopes k (predicted vs. observed inhibition) and k' (observed vs. predicted inhibition) of the regression lines for the QSAR models; Table S7. Details of Y-randomization test performed on the QSAR models; Figure S1. Chemical space analysis of QSAR studies (Table 5) for Protease...
Using hybrid GA-ANN to predict biological activity of HIV protease inhibitors
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Abstract The prediction of biological activity of a chemical compound from its structural features, representing its physico-chemical properties, plays an important role in drug discovery, design and development. Since the biological data is highly non-linear, the machine-learning techniques have been widely used for modeling it. In the present work, the clustering, genetic algorithm (GA) and artificial neural networks (ANN) are used to develop computational prediction models on a dataset of HIV protease inhibitors.
Karbala international journal of modern science, 2024
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