original ) (raw )Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones
Kunal Roy
Journal of Enzyme Inhibition and Medicinal Chemistry, 2008
View PDFchevron_right
A quantitative structure–activity relationship study on HIV-1 integrase inhibitors using genetic algorithm, artificial neural networks and different statistical methods
SHAHAB SHARIATI
Arabian Journal of Chemistry, 2016
View PDFchevron_right
Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives
Didier Villemin
European Journal of Medicinal Chemistry, 2010
View PDFchevron_right
QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression
BRAHIM MINAOUI
Arabian Journal of Chemistry, 2017
View PDFchevron_right
A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis
A Afantitis
Molecular Diversity, 2006
View PDFchevron_right
Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition
sunil kumar
Journal of Molecular Graphics and Modelling, 2010
View PDFchevron_right
Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives
Kunal Roy
European Journal of Medicinal Chemistry, 2009
View PDFchevron_right
QSAR Study on Anti-HIV-1 Activity of 4-Oxo-1,4-dihydroquinoline and 4-Oxo-4H-pyrido[1,2-a]pyrimidine Derivatives Using SW-MLR, Artificial Neural Network and Filtering Methods
Amin ranjbar
Iranian journal of pharmaceutical research (IJPR)
View PDFchevron_right
Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method
Giuseppina C Gini
Central European Journal of Chemistry, 2013
View PDFchevron_right
QSAR modeling of anti-HIV activity for DAPY-like derivatives using the mixture of ligand-receptor binding information and functional group features as a new class of descriptors
Davood Shahsavani
Network Modeling Analysis in Health Informatics and Bioinformatics, 2020
View PDFchevron_right
Identification of LOGP values and Electronegativities as structural insights to model inhibitory activity of HIV-1 Capsid Inhibitors - A SVM and MLR aided QSAR studies.
Anuraj Nayarisseri
Current Topics in Medicinal Chemistry. 2012 Aug 1;12(16):1763-74., 2012
View PDFchevron_right
IJERT-Modeling of Activity of Cyclic Urea HIV-1 Protease Inhibitors using QSAR
IJERT Journal
International Journal of Engineering Research and Technology (IJERT), 2021
View PDFchevron_right
Neural Networks :Application for prediction of Anti-HIV-1 Activity of HEPT Derivatives
Didier Villemin
View PDFchevron_right
Using hybrid GA-ANN to predict biological activity of HIV protease inhibitors
Sunil Kumar
2008
View PDFchevron_right
Exploring QSAR of non-nucleoside reverse transcriptase inhibitors by artificial neural networks: HEPT derivatives
Mohamed Zahouily
Arkivoc, 2007
View PDFchevron_right
Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors
Nung Kion Lee
Pre-print, 2021
View PDFchevron_right
Evaluation of potential HIV-1 reverse transcriptase inhibitors by artificial neural networks
Vsevolod Tanchuk
Proceedings of IEEE Symposium on Computer-Based Medical Systems (CBMS)
View PDFchevron_right
Modeling of Activity of Cyclic Urea HIV-1 Protease Inhibitors using QSAR
IJERT Journal
View PDFchevron_right
Exploring QSAR of Non-Nucleoside Reverse Transcriptase Inhibitors by Neural Networks: TIBO Derivatives
Didier Villemin
International Journal of Molecular Sciences, 2004
View PDFchevron_right
Artificial neural networks: Non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors
Didier Villemin
Molecular Diversity, 2004
View PDFchevron_right
Machine learning algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery
Hamid Toufik
2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), 2019
View PDFchevron_right
In-silico Discovery and Simulated Selection of Multi-target Anti-HIV-1 Inhibitors
Christian C H I N W E U B A Onoyima
International Research Journal of Pure and Applied Chemistry, 2016
View PDFchevron_right
QSAR modelling of HIV1 reverse transcriptase inhibition by benzoxazinones using a combination of P_VSA and pharmacophore feature descriptors
KARTHIKEYAN CHANDRABOSE
Bioorganic & Medicinal Chemistry Letters, 2004
View PDFchevron_right
In Silico SAR Studies of HIV-1 Inhibitors
Alia Tadjer
Pharmaceuticals (Basel, Switzerland), 2018
View PDFchevron_right
3D-QSAR and SVM Prediction of BRAF-V600E and HIV Integrase Inhibitors: A Comparative Study and Characterization of Performance with a New Expected Prediction Performance Metric
Leonard Wesley
American Journal of Biochemistry and Biotechnology, 2016
View PDFchevron_right