Ann Qsar Workflow for Predicting the Inhibition of HIV-1 Reverse Transcriptase by Pyridinone Non-Nucleoside Derivatives

Barzegar, Abolfazl; Zamani-Gharehchamani, Elham; Kadkhodaie, Ali

doi:10.4155/fmc-2017-0040

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…The authors concluded that the PaD and profile methods were most stable [ 84 ]. There are several subtypes of ANNs such as feed-forward backpropagation network (BP-NN), radial basis function networks and probabilistic neural networks, and linear regression was combined with nonlinear BP-NN-QSAR model to investigate inhibitory activities of pyridinone derivatives with HIV-1 reverse transcriptase; the results showed that the model was robust and cost-effective for pIC50 estimation, capable of prediction of complex relationships [ 85 ]. Previously, it has been presented as three molecular fingerprints (namely FP2, MACCS, and ECFP6) combined ANN-QSAR (FANN-QSAR) to predict biological activities of cannabinoid ligands; after validation, ECFP6-ANN-QSAR required no alignment in the training process, and its performance consistently across diverse data sets was better than others [ 82 , 86 ].…”

Section: Ligand-based Virtual Screeningmentioning

confidence: 99%

Opportunities and challenges in application of artificial intelligence in pharmacology

et al. 2023

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Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

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Section: Ligand-based Virtual Screeningmentioning

confidence: 99%

Opportunities and challenges in application of artificial intelligence in pharmacology

et al. 2023

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“…The various steps of QSAR/QSPR analysis including: Selection of Data set, calculation of molecular descriptors, descriptive analysis (linear/ nonlinear methods), illustrate the results of statistical analysis (prediction and evaluation of models) and suggestion of novel compounds [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

QSPR models to predict quantum chemical properties of imidazole derivatives using genetic algorithm–multiple linear regression and back‐propagation–artificial neural network

Moshayedi

Shafiei

Isfahani

2022

Int J of Quantum Chemistry

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Imidazole derivatives are the foundation of different types of drugs with a wide range of biological activities. In this study, the genetic algorithm–multiple linear regression (GA–MLR), and backpropagation–artificial neural network (BP–ANN) were applied to design QSPR models to predict the quantum chemical properties like the entropy (S) and enthalpy of formation (∆Hf) of imidazole derivatives. In order to draw molecular structure of 84 derivative compounds Gauss View 05 program was used. These structures were optimized at DFT‐B3LYP/6‐311G* level with Gaussian09W. The Dragon software was used to calculate a set of different molecular descriptors, and the genetic algorithm procedure and backward stepwise regression were applied for the selection of descriptors. The resulting quantitative GA–MLR model of ∆Hf, showed that there is good linear correlation between the selected descriptors and ∆Hf of compounds. Also the results show that the BP–ANN model appeared to be superior to GA–MLR model for prediction of entropy. Different internal and external validation metrics were adopted to verify the predictive performance of QSPR models. The predictive powers of the models were found to be acceptable. Thus, these QSPR models may be useful for designing new series of imidazole derivatives and prediction of their properties.

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“…[12][13][14][15][16] The QSAR model attempts to¯nd the mathematical relationships between molecular structure and biological activity. 17,18 The computer-aided drug design Table 2 are only di®erent in R 1 , R 2 and R 3 substituents of A and B Aryl rings.…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

Barzegar

Hamidi

2017

J. Theor. Comput. Chem.

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Human immunode¯ciency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Di®erent quantitative structure-activity relationships (QSARs) algorithms have been used in order to develop e±cient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four di®erent approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA-MLR and stepwise PCA-MLR were utilized for this aim. Twenty two di®erent sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the \concrete" and \stepwise" procedure in two di®erent algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA-MLR model showed that the method has higher ability to predict the compounds' activity. The stepwise PCA-MLR model showed highly validated statistical results both in¯tting and prediction processes (R 2 test ¼ 0:78 and Q 2 ¼ 0:80). Therefore, using stepwise PCA approach is suitable to remove ine®ective descriptors, which results in remaining e±cient descriptors for building good predictability stepwise PCA-MLR. The stepwise hybrid approach of PCA-MLR may be useful in derivation of highly predictive and interpretable QSAR models.

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Ann Qsar Workflow for Predicting the Inhibition of HIV-1 Reverse Transcriptase by Pyridinone Non-Nucleoside Derivatives

Abstract: The nonlinear ANN-QSAR model based on the topological polarizability, geometrical steric, hydrophobicity and substituted benzene functional group indices might be able to help for designing novel pyridinone NNRTIs.

Cited by 9 publications

References 33 publications

Opportunities and challenges in application of artificial intelligence in pharmacology

Opportunities and challenges in application of artificial intelligence in pharmacology

QSPR models to predict quantum chemical properties of imidazole derivatives using genetic algorithm–multiple linear regression and back‐propagation–artificial neural network

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

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