R-group template CoMFA combines benefits of "ad hoc" and topomer alignments using 3D-QSAR for lead optimization

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm

The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Modeling MEK4 Kinase Inhibitors through Perturbed Electrostatic Potential Charges

MEK4, mitogen-activated protein kinase kinase 4, is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. With advances in both computer and biological high-throughput screening, selective chemical entities can be discovered. Structure-based quantitative structure-activity relationship (QSAR) modeling often fails to generate accurate models due to poor alignment of training sets containing highly diverse compounds. Here we describe a highly predictive, nonalignment based robust QSAR model based on a data set of strikingly diverse MEK4 inhibitors. We computed the electrostatic potential (ESP) charges using a density functional theory (DFT) formalism of the donor and acceptor atoms of the ligands and hinge residues. Novel descriptors were then generated from the perturbation of the charge densities of the donor and acceptor atoms and were used to model a diverse set of 84 compounds, from which we built a robust predictive model.

Two- and three-dimensional QSAR studies on hURAT1 inhibitors with flexible linkers: topomer CoMFA and HQSAR

hURAT1 (human urate transporter 1) is a successful target for hyperuricemia. Recently, the modification work on hURAT1 inhibitors showed that the flexible linkers would benefit biological activity. The study aimed to investigate the contribution of the linkers and give modification strategies on this kind of structures based on QSAR models (HQSAR and topomer CoMFA). The most effective HQSAR and topomer CoMFA models were generated by applying the training set containing 63 compounds, with the cross-validated q² values of 0.869/0.818 and the non-cross-validated correlation coefficients r² of 0.951/0.978, respectively. The Y-randomization test was applied to ensure the robustness of the models. The external predictive correlation coefficient (r_pred²) grounded on the external test set (21 compounds) of two models was 0.910 and 0.907, respectively. In addition, the models were validated by Golbraikh-Tropsha and Roy methods, as well as other statistical metrics. The results showed that both models were reliable. Topomer CoMFA steric/electrostatic contours and HQSAR atomic contribution maps illustrated the structural features which governed their inhibitory potency. The dependable results could provide important insights to guide the designing of more potential hURAT1 inhibitors.

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties

We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.

Homology modeling of FFA2 identifies novel agonists that potentiate insulin secretion

Critical aspects of maintaining glucose homeostasis in the face of chronic insulin resistance and type 2 diabetes (T2D) are increased insulin secretion and adaptive expansion of beta cell mass. Nutrient and hormone sensing G protein-coupled receptors are important mediators of these properties. A growing body of evidence now suggests that the G protein-coupled receptor, free fatty acid receptor 2 (FFA2), is capable of contributing to the maintenance of glucose homeostasis by acting at the pancreatic beta cell as well as at other metabolically active tissues. We have previously demonstrated that Gα_q/11-biased agonism of FFA2 can potentiate glucose stimulated insulin secretion (GSIS) as well as promote beta cell proliferation. However, the currently available Gα_q/11-biased agonists for FFA2 exhibit low potency, making them difficult to examine in vivo. This study sought to identify Gα_q/11-biased FFA2-selective agonists with potent GSIS-stimulating effects. To do this, we generated an FFA2 homology model that was used to screen a library of 10 million drug-like compounds. Although FFA2 and the related short chain fatty acid receptor FFA3 share 52% sequence similarity, our virtual screen identified over 50 compounds with predicted selectivity and increased potency for FFA2 over FFA3. Subsequent in vitro calcium mobilization assays and GSIS assays resulted in the identification of a compound that can potentiate GSIS via activation of Gα_q/11 with 100-fold increased potency compared with previously described Gα_q/11-biased FFA2 agonists. These methods and findings provide a foundation for future discovery efforts to identify biased FFA2 agonists as potential T2D therapeutics.

Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors

Aim: Sodium–glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. Materials & methods: In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure–activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. Results: The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. Conclusion: The models and docking provided important insights into the design of potent inhibitors for SGLT2.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Design Some New Type-I c-met Inhibitors Based on Molecular Docking and Topomer CoMFA Research

In this paper, a specific design strategy targeting c-met kinase was reported based on docking modeling and topomer comparative molecular field analysis (Topomer CoMFA). A novel U-shape conformation which is distinct from the literature was demonstrated by molecular docking among 68 U-shape c-met inhibitors. According to the docking results, two Topomer CoMFA models with high predictive ability were established based on the two fragment rule. The results from both docking and topomer CoMFA showed that the π-π stacking interaction with Tyr1230 and the hydrogen bond with hinge region play an important role in inhibitory activity. Furthermore, the flexible linker and the adjacent solvent group would be favorable to stabilize the conformation and to enhance the two interactions mentioned above. Based on our patent, 14 new compounds were designed by our design strategy. The binding mode exhibited as expected and their activities were predicted by topomer CoMFA model. The preliminary biological tests showed most of them have potent activity to c-met kinase. Our study would provide guidelines to design some new U-shaped c-met inhibitors with new scaffolds and optimize the current molecules.

A 3D QSAR study of betulinic acid derivatives as anti-tumor agents using topomer CoMFA: model building studies and experimental verification

Betulinic acid (BA) is a natural product that exerts its cytotoxicity against various malignant carcinomas without side effects by triggering the mitochondrial pathway to apoptosis. Betulin (BE), the 28-hydroxyl analog of BA, is present in large amounts (up to 30% dry weight) in the outer bark of birch trees, and shares the same pentacyclic triterpenoid core as BA, yet exhibits no significant cytotoxicity. Topomer CoMFA studies were performed on 37 BA and BE derivatives and their in vitro anti-cancer activity results (reported as IC₅₀ values) against HT29 human colon cancer cells in the present study. All derivatives share a common pentacyclic triterpenoid core and the molecules were split into three pieces by cutting at the C-3 and C-28 sites with a consideration toward structural diversity. The analysis gave a leave-one-out cross-validation q² value of 0.722 and a non-cross-validation r² value of 0.974, which suggested that the model has good predictive ability (q² > 0.2). The contour maps illustrated that bulky and electron-donating groups would be favorable for activity at the C-28 site, and a moderately bulky and electron-withdrawing group near the C-3 site would improve this activity. BE derivatives were designed and synthesized according to the modeling result, whereby bulky electronegative groups (maleyl, phthalyl, and hexahydrophthalyl groups) were directly introduced at the C-28 position of BE. The in vitro cytotoxicity values of the given analogs against HT29 cells were consistent with the predicted values, proving that the present topomer CoMFA model is successful and that it could potentially guide the synthesis of new betulinic acid derivatives with high anti-cancer activity. The IC₅₀ values of these three new compounds were also assayed in five other tumor cell lines. 28-O-hexahydrophthalyl BE exhibited the greatest anti-cancer activities and its IC₅₀ values were lower than those of BA in all cell lines, excluding DU145 cells.