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

[1] Amini Z(2016)Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones Comput Biol Chem 64 335-345

[2] Fatemi MH(2013)QSAR study of necroptosis inhibitory activities (EC 50) of [1, 2, 3] thiadiazole and thiophene derivatives using Bayesian regularized artificial neural network and calculated descriptors Med Chem Res 22 392-400

[3] Gharaghani S(2007)Prediction of cytotoxicity data (CC50) of anti-HIV 5-pheny-l-phenylamino-1H-imidazole derivatives by artificial neural network trained with Levenberg–Marquardt algorithm J Mol Gr Model 26 360-367

[4] Arab Chamjangali M(2007)QSAR analysis of some phthalimide analogues based inhibitors of HIV-1 integrase ARKIVOC 15 66-81

[5] Ashrafi M(2015)QSAR and pharmacophore modeling of indole-based C-3 pyridone compounds as HCV NS5B polymerase inhibitors utilizing computed molecular descriptors Med Chem Res 24 2432-2440

[6] Arab Chamjangali CM(2020)Combination of radial distribution functions as structural descriptors with ligand-receptor interaction information in the QSAR study of some 4-anilinoquinazoline derivatives as potent EGFR inhibitors Struct Chem 28 235-242

[7] Beglari M(2000)Th e protein data bank Nucleic Acids Res 10 2684-2692

[8] Bagherian G(2014)Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition Mol Biosyst 33 35-43

[9] Bansal R(2012)Results of molecular docking as descriptors to predict human serum albumin binding affinity J Mol Gr Model 46 93-104

[10] Karthikeyan C(2013)Combining structure-and ligand-based approaches for studies of interactions between different conformations of the hERG K+ channel pore and known ligands J Mol Gr Model 11 733-961

[1] Amini Z(2016)Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones Comput Biol Chem 64 335-345

[2] Fatemi MH(2013)QSAR study of necroptosis inhibitory activities (EC 50) of [1, 2, 3] thiadiazole and thiophene derivatives using Bayesian regularized artificial neural network and calculated descriptors Med Chem Res 22 392-400

[3] Gharaghani S(2007)Prediction of cytotoxicity data (CC50) of anti-HIV 5-pheny-l-phenylamino-1H-imidazole derivatives by artificial neural network trained with Levenberg–Marquardt algorithm J Mol Gr Model 26 360-367

[4] Arab Chamjangali M(2007)QSAR analysis of some phthalimide analogues based inhibitors of HIV-1 integrase ARKIVOC 15 66-81

[5] Ashrafi M(2015)QSAR and pharmacophore modeling of indole-based C-3 pyridone compounds as HCV NS5B polymerase inhibitors utilizing computed molecular descriptors Med Chem Res 24 2432-2440

[6] Arab Chamjangali CM(2020)Combination of radial distribution functions as structural descriptors with ligand-receptor interaction information in the QSAR study of some 4-anilinoquinazoline derivatives as potent EGFR inhibitors Struct Chem 28 235-242

[7] Beglari M(2000)Th e protein data bank Nucleic Acids Res 10 2684-2692

[8] Bagherian G(2014)Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition Mol Biosyst 33 35-43

[9] Bansal R(2012)Results of molecular docking as descriptors to predict human serum albumin binding affinity J Mol Gr Model 46 93-104

[10] Karthikeyan C(2013)Combining structure-and ligand-based approaches for studies of interactions between different conformations of the hERG K+ channel pore and known ligands J Mol Gr Model 11 733-961