Introduction to 3d Qsar Model

What is/are 3d Qsar Model?

3d Qsar Model - 541) values were obtained for developed 3D QSAR model. ^[1] Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2A). ^[2] The present study is focused on evolving multiple 2D and 3D QSAR models with good statistical fit based on the structures of 59, Thieno[2,3-d]pyrimidin-4-yl hydrazones with diverse CDK4 inhibitory activity profile. ^[3] 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. ^[4] Conformation independent QSAR models were developed for three random splits and a 3D QSAR model for one random split into the training and test sets. ^[5] Objective: The main objective of the present study was to evolve a novel pharmacophore of methaniminium derivatives as factor Xa inhibitors by developing best 2D and 3D QSAR models. ^[6] The 3D QSAR model was developed using known bioactive compounds against the PvM18AAP protein which statistically signify the k-NN model with q^2 = 0. ^[7] One random split of the initial data into the training and test sets was used to establish the 3D QSAR model, while for establishing conformation independent models three random splits into the training and test sets were used. ^[8] Finally, this study of molecular docking, ADME/T prediction and the construction of 3D QSAR model were performed. ^[9] The anti-tubercular activity of the designed compounds was predicted using the generated 3D QSAR models. ^[10] Intriguingly, a few plant-based drugs have paved their way to phase II clinical trial, inspired by which, the present study essayed to unearth novel ALK inhibitors from the NPACT database which comprises 1574 plant-derived compounds that exhibit anti-cancerous activity, using 3D QSAR model (AAADD. ^[11] Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q2 (‘leave-many-out’) is equal to 0. ^[12] The development of 3D QSAR model of N1,N4-bis(2-oxoindolin-3-ylidene) succinohydrazides using the stepwise-backward variable methods to generate Multiple Linear Regression method to elucidate the structural properties required for EGFR inhibitory activity and also perform the Molecular Docking studies on EGFR (PDB ID:1M17). ^[13] The selected 3D QSAR models revealed the importance of hydrogen bond acceptors, hydrogen bond donors and aromatic rings for selectively towards the target enzyme. ^[14] In the activity 3D QSAR modeling, predictivity of the BACE pKd model was modest, while for the second target (Cathepsin-S), leading performance was achieved. ^[15] For the atom based-3D QSAR model, a good cross-validation correlation coefficient i. ^[16] 4 were selected for building 3D QSAR model. ^[17] A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. ^[18] We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. ^[19] METHODS A series of C-7-hydroxyproton substituted chrysin derivatives were virtually drawn to generate a small compound library that was screened using 3D QSAR model created from forty-two known EGFR tyrosine kinase inhibitors. ^[20]

Developed 3d Qsar Model

541) values were obtained for developed 3D QSAR model. ^[1] We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. ^[2]