Toward a hierarchical virtual screening and toxicity risk analysis for identifying novel CA XII inhibitors

Pharmacophore modeling in drug design

A successful and expanded area of computational drug design is pharmacophore modeling. A pharmacophore is a description of the structural features of a compound that are essential to its biological activity. The rational design of new drugs has made extensive use of the pharmacophore concept. By schematically illustrating the essential components of molecular recognition, Pharmacophores can be used to represent and identify molecules in two or three dimensions. Besides target identification, the pharmacophore concept is also helpful for side effects, off-target, and absorption, distribution, and toxicity modeling. Moreover, to enhance virtual screening, pharmacophores, and molecular docking simulations are frequently coupled. A completely new area of drug design has been made possible by the development of machine learning techniques and pharmacophore mapping algorithms, wherein an ineffective molecule with the right modifications may have the potential to function as an inhibitor. This approach has been stimulated by its predictive abilities to assess the possibility that a set of compounds will be active against protein targets of interest. With alignment to the standard pharmacophore model, active compounds of the protein target can be developed. The pharmacophore modeling/screening technique is used to identify possible proteins of interest and seek out/suggest novel therapeutic uses for the drug.

Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors

Cyclooxygenase-2 (COX-2) enzyme inhibitors have not eliminated the necessity for developed drugs not only in the nonsteroidal anti-inflammatory drug (NSAIDs) area, but also in other therapeutic applications including prevention of cancer and Alzheimer's disease. A series of novel substituted cyclic imides have been reported as selective COX-2 inhibitors. To understand the structural features responsible for their activity, a 3D validated pharmacophore and quantitative structure−activity relationship (QSAR) model have been developed. The values of enrichment factor (EF), goodness of hit score (GH), area under the ROC curve (AUC), sensitivity, and specificity refer to the good ability of the pharmacophore model to identify active compounds. Multiple linear regression (MLR) produced statistically significant QSAR model with (R²_training = 0.763, R²_test = 0.96) and predictability (Q²_training = 0.66, Q²_test = 0.84). Then, using the pharmacophore and QSAR models, eight authenticated botanicals in two herbal medicines and the ZINC compounds database, were virtually screened for ligands to COX-2. The retrieved hits which also obey lipinski's rule of five (RO5) were docked in the COX-2 3D structure to investigate their binding mode and affinity. Finally, based on the docking results, nine molecules were prioritized as promising hits that could be used as leads to discover novel COX-2 inhibitors. COX-2 inhibition of most of these hits has not been reported previously. Ten-nanosecond molecular dynamics simulation (10-ns MD) was performed on the initial structure COX-2 complex with ZINC000113253375 and ZINC000043170560 resulted from the docking. Our utilization of the 3D pharmacophore model, QSAR, molecular docking, and molecular dynamics simulation trials can be a potent strategy to successfully predict activity, efficiently design drugs, and screen large numbers of new compounds as active drug candidates.

Clustering and Sampling of the c-Met Conformational Space: A Computational Drug Discovery Study

BACKGROUND

c-Met kinase plays a critical role in a myriad of human cancers, and a massive scientific work was devoted to design more potent inhibitors.

OBJECTIVE

In this study, 16 molecular dynamics simulations of different complexes of potent c-Met inhibitors with U-shaped binding mode were carried out regarding the dynamic ensembles to design novel potent inhibitors.

METHODS

A cluster analysis was performed, and the most representative frame of each complex was subjected to the structure-based pharmacophore screening. The GOLD docking program investigated the interaction energy and pattern of output hits from the virtual screening. The most promising hits with the highest scoring values that showed critical interactions with c-Met were presented for ADME/Tox analysis.

RESULTS

The screening yielded 45,324 hits that all of them were subjected to the docking studies and 10 of them with the highest-scoring values having diverse structures were presented for ADME/Tox analyses.

CONCLUSION

The results indicated that all the hits shared critical Pi-Pi stacked and hydrogen bond interactions with Tyr1230 and Met1160 respectively.

A novel proteochemometrics model for predicting the inhibition of nine carbonic anhydrase isoforms based on supervised Laplacian score and k-nearest neighbour regression

Carbonic anhydrases (CAs) are essential enzymes in biological processes. Prediction of the activity of compounds towards CA isoforms could be evaluated by computational techniques to discover a novel therapeutic inhibitor. Studies such as quantitative structure-activity relationships (QSARs), molecular docking and pharmacophore modelling have been carried out to design potent inhibitors. Unfortunately, QSAR does not consider the information of target space in the model. We successfully developed an in silico proteochemometrics model that simultaneously uses target and ligand descriptors to predict the activities of CA inhibitors. Herein, a strong predictive model was built for the prediction of protein-ligand binding affinity between nine human CA isoforms and 549 ligands. We applied descriptors obtained from the PROFEAT webserver for the proteins. Ligands were encoded by descriptors from PaDEL-Descriptor software. Supervised Laplacian score (SLS) and particle swarm optimization were used for feature selection. Models were derived using k-nearest neighbour (KNN) regression and a kernel smoother model. The predictive ability of the models was evaluated by an external validation test. Statistical results (Q²_ext = 0.7806, r²_test = 0.7811 and RMSE_test = 0.5549) showed that the model generated using SLS and KNN regression outperformed the other models. Consequently, the selectivity of compounds towards these enzymes will be predicted prior to synthesis.