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

Integrating docking scores and key interaction profiles to improve the accuracy of molecular docking: towards novel B-RafV600E inhibitors

A set of ninety-eight B-RafV600E inhibitors was used for the development of a molecular docking based QSAR model using linear and non-linear regression models. The integration of docking scores and key interaction profiles significantly improved the accuracy of the QSAR models, providing reasonable statistical parameters (Rtrain2 = 0.935, Rtest2 = 0.728 and QCV2 = 0.905). The established MD-SVR (molecular docking based SMV regression) model as well as model screening of a natural product database was carried out and two natural products (quercetin and myricetin) with good prediction activities were biologically evaluated. Both compounds exhibited promising B-RafV600E inhibitory activities (ICQuercetin50 = 7.59 μM and ICMyricetin50 = 1.56 μM), suggesting a high reliability and good applicability of the established MD-SVR model in the future development of B-RafV600E inhibitors with high efficacy.

Discriminatory analysis based molecular docking study for in silico identification of epigallocatechin-3-gallate (EGCG) derivatives as B-RafV600E inhibitors

Virtual screening and biological testing were utilized to identify novel B-Raf^V600E inhibitors.

Discovery of a potent, orally available dual CysLT₁ and CysLT₂ antagonist with dicarboxylic acid

A potent, orally available dual CysLT₁ and CysLT₂ receptor antagonist with a dicarboxylic acid is described. 4-(3-(Carboxymethyl)-4-{(E)-2-[4-(4-phenoxybutoxy)phenyl]vinyl}-1H-indol-1-yl)butanoic acid (15: ONO-4310321, IC₅₀: CysLT₁=13nM, CysLT₂=25 nM) showed excellent pharmacokinetic profiles (%Frat=100) compared with our previously reported compound 1 (%Frat=1.5). In addition, we describe a new rule for dicarboxylic acid derivatives to show good oral bioavailability (%Frat⩾40) in rats (HBDs: ⩽2, ClogP: >6.5 and TPSA: <100). Especially, reduction of only one hydrogen-bond donor (HBDs) showed dramatically improved oral bioavailability. This small change of HBDs in dicarboxylic acid derivatives is generally a very effective modification.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Support vector machine (SVM) classification model based rational design of novel tetronic acid derivatives as potent insecticidal and acaricidal agents

A novel SVM classification model was constructed and applied in the development of novel tetronic acid derivatives as potent insecticidal and acaricidal agents.

Discovery of Highly Potent Dual CysLT1 and CysLT2 Antagonist

The benzoxazine derivative, (2S)-4-(3-carboxypropyl)-8-{[4-(4-phenylbutoxy)benzoyl]amino}-3,4-dihydro-2H-1,4-benzoxazine-2-carboxylic acid (19, ONO-2050297), was identified as the first potent dual CysLT1 and CysLT2 antagonist with IC50 values of 0.017 μM (CysLT1) and 0.00087 μM (CysLT2), respectively.