Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

Abstract

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC 50 values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.

QSAR model as a random event: A case of rat toxicity

“…The prediction can be done by means of correlations between descriptors and endpoints. [1][2][3][4][5][6][7][8][9][10] There are various software tools available to build QSPR/QSAR models, 11-15 but there is no standard procedure for the validation of QSPR/QSAR models. According to the Guidance document on the validation of QSAR models developed by the Organisation for Economic Co-operation and Development (OECD) the validation of a models is an important element of a QSPR/QSAR analyses.…”

Consensus Predictive Model for Human K562 Cell Growth Inhibition through Enalos Cloud Platform

“…To validate the performance of the model, the following criteria were calculated [Eqs. ]: …”

“…Finally, d> and σ were calculated as the average and standard deviation, respectively, of all distances included in this set. Z is an empirical cutoff, and its value was set to 0.5 . The Enalos+ Domain‐Similarity node is included in our workflow and was used to assess the domain of applicability of the proposed model …”

Integrated pharmacophore and docking‐based designing of dual inhibitors of aldose reductase (ALR2) and protein tyrosine phosphatase 1B (PTP1B) as novel therapeutics for insulin‐resistant diabetes and its complications

“…The predictive scope of the generated model was analyzed by calculating applicability domain (APD) . It defines the similarity threshold between training and test set compounds.…”