Pharmacophore identification, synthesis, and biological evaluation of carboxylated chalcone derivatives as CysLT1 antagonists

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two groups: highly active molecules (IC₅₀ < 1000 nM) and weakly active molecules (IC₅₀ ≥ 1000 nM). The molecules were characterized by several descriptors including CORINA descriptors, MACCS fingerprints, Morgan fingerprint and molecular SMILES. For CORINA descriptors and two types of fingerprints, we used the random forests (RF) and deep neural networks (DNN) to build models. For molecular SMILES, we used recurrent neural networks (RNN) with the self-attention to build models. The accuracies of test sets for all models reached 85%, and the accuracy of the best model (Model 2C) was 93%. In addition, we made structure-activity relationship (SAR) analyses on CysLT1 receptor antagonists, which were based on the output from the random forest models and RNN model. It was found that highly active antagonists usually contained the common substructures such as tetrazoles, indoles and quinolines. These substructures may improve the bioactivity of the CysLT1 receptor antagonists.

Discovery of 3-Substituted 1H-Indole-2-carboxylic Acid Derivatives as a Novel Class of CysLT1 Selective Antagonists

The indole derivative, 3-((E)-3-((3-((E)-2-(7-chloroquinolin-2yl)vinyl)phenyl)amino)-3-oxoprop-1-en-1-yl)-7-methoxy-1H-indole-2-carboxylic acid (17k), was identified as a novel and highly potent and selective CysLT1 antagonist with IC50 values of 0.0059 ± 0.0011 and 15 ± 4 μM for CysLT1 and CysLT2, respectively.

Structure-based drug design using GPCR homology modeling: toward the discovery of novel selective CysLT2 antagonists

3D structure of CysLT2 receptor was constructed by using homology modeling and molecular simulations. The binding pocket of CysLT2 receptor and the proposition of the interaction mode between CysLT2 and HAMI3379 were identified. A series of dicarboxylated chalcones was then virtually evaluated through molecular docking studies. A total of six compounds 13a-f with preferable scores was further synthesized and tested for CysLT2 antagonistic activities by determination of the cytosolic free Ca(2+) levels in HEK293 cells. Compounds 13e and 13f exhibited potent and selective CysLT2 antagonistic activities with IC50 values being 7.5 and 0.25 μM, respectively.