Molecular modeling study on chemically diverse series of cyclooxygenase-2 selective inhibitors: generation of predictive pharmacophore model using Catalyst

Abstract

Cyclooxygenase (COX) enzymes catalyse the biosynthesis of prostaglandins and thromboxane from arachidonic acid (AA). We summarize in this paper, the development of pharmacophores of a dataset of inhibitors for COX-2 by using the Catalyst/Hypogen module using six chemically diverse series of compounds. Training set consisting of 24 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 10000 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, one hydrogen bond donor, one hydrogen bond acceptor, one hydrophobic aliphatic and one ring aromatic feature, had a correlation (r) of 0.954 and a root mean square deviation of 0.894. The entropy (configuration cost) value of the hypotheses was 16.79, within the allowed range. The difference between the null hypothesis and the fixed cost and between the null hypothesis and the total cost of the best hypothesis (hypothesis 1) was 88.37 and 78.51, respectively. The model was validated on a test set consisting of six different series of structurally diverse 22 compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug like molecules to retrieve new chemical entities as potent COX-2 inhibitors. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.

Pharmacophore Modeling and Virtual Screening Studies of Checkpoint Kinase 1 Inhibitors

“…The performance of pharmacophore model was examined by utilizing the pharmacophore model to regress against the test set compounds. The cross validation was carried out by using CatScramble 35) program within CATALYST. This procedure tries to scramble the experimental activities in the training set randomly, and the resulting training sets are used for HypoRefine runs.…”

Insight into analysis of interactions of GW9508 to wild-type and H86F and H137F GPR40: A combined QM/MM study and pharmacophore modeling

“…2, were selected as a training set to develop the pharmacophore model. Conformational models for the molecules were developed by poling algorithm, which seeks to provide a broad coverage of conformational space using the best conformer generation method with a maximum conformational energy of 20 kcal/mol [38]. The number of conformers generated for each compound was limited to a maximum of 250.…”

Pharmacophore Modeling and Virtual Screening of Novel Inhibitors for c‐Kit Kinase

“…14 In brief, all compounds were built in 2D/3D Visualizer within CATALYST 4.11 and minimized to the closest local minimum using the CHARMm-like force field incorporated in the CATALYST program. A series of energetically reasonable conformational models which represent the flexibility of each compound were generated by CatConf program within CATALYST.…”