Haichun Liu

An Integrated Virtual Screening Approach for VEGFR-2 Inhibitors

In recent years, various virtual screening (VS) tools have been developed, and many successful screening campaigns have been showcased. However, whether by conventional molecular docking or pharmacophore screening, the selection of virtual hits is based on the ranking of compounds by scoring functions or fit values, which remains the bottleneck of VS due to insufficient accuracy. As the limitations of individual methods persist, a comprehensive comparison and integration of different methods may provide insights into selecting suitable methods for VS. Here, we evaluated the performance of molecular docking, fingerprint-based 2D similarity and multicomplex pharmacophore in an individual and a combined manner, through a retrospective VS study on VEGFR-2 inhibitors. An integrated two-layer workflow was developed and validated through VS of VEGFR-2 inhibitors against the DUD-E database, which demonstrated improved VS performance through a ligand-based method ECFP_4, followed by molecular docking, and then a strict multicomplex pharmacophore. Through a retrospective comparison with six published papers, this integrated approach outperformed 43 out of 45 methods, indicating a great effectiveness. This kind of integrated VS approach can be extended to other targets for the screening and discovery of inhibitors.

Recent Advances in the Research and Development of B-Raf Inhibitors

Oncogenic B-Raf has been identified in a variety of cancers with high incidence, especially in malignant melanoma and thyroid cancer. Most B-Raf mutations elicit elevated kinase activity and the constitutive activation of Ras/Raf/MEK/ERK pathway, which induces proliferation and promotes malignant transformation. Therefore, B-Raf inhibitors, targeting B-Raf or mutated B-Raf, have received increasing momentum in oncology drug discovery arena. This review focuses on the diverse small-molecule inhibitors of B-Raf kinase recently reported in the literature, including those currently in clinical and preclinical phase. They are described as two categories, type I or type II kinase inhibitors, based on their different mechanism of action with active or inactive conformations of the B-Raf kinase derived from the available crystal structures or molecular docking analysis. A particular emphasis is placed on their binding modes and the structure-activity relationship (SAR) of each chemical structure class.

Novel Strategy for Three-Dimensional Fragment-Based Lead Discovery

Fragment-based drug design (FBDD) is considered a promising approach in lead discovery. However, for a practical application of this approach, problems remain to be solved. Hence, a novel practical strategy for three-dimensional lead discovery is presented in this work. Diverse fragments with spatial positions and orientations retained in separately adjacent regions were generated by deconstructing well-aligned known inhibitors in the same target active site. These three-dimensional fragments retained their original binding modes in the process of new molecule construction by fragment linking and merging. Root-mean-square deviation (rmsd) values were used to evaluate the conformational changes of the component fragments in the final compounds and to identify the potential leads as the main criteria. Furthermore, the successful validation of our strategy is presented on the basis of two relevant tumor targets (CDK2 and c-Met), demonstrating the potential of our strategy to facilitate lead discovery against some drug targets.

Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors

AbstractMesenchymal epithelial transition factor (c-Met) is an attractive target for cancer therapy. Three-dimensional pharmacophore hypotheses were built based on a set of known structurally diverse c-Met inhibitors. The best pharmacophore model, which identified inhibitors with an associated correlation coefficient of 0.983 between their experimental and estimated IC50 values, consisted of two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic feature. The highly predictive power of the model was rigorously validated by test set prediction and Fischer’s randomization method. The high values of enrichment factor and receiver operating characteristic (ROC) score indicated the model performed fairly well at distinguishing active from inactive compounds. The model was then applied to screen compound database for potential c-Met inhibitors. A filtering protocol, including druggability and molecular docking, were also applied in hits selection. The final 38 molecules, which exhibited good estimated activities, desired binding mode and favorable drug likeness were identified as potential c-Met inhibitors. Their novel backbone structures could be served as scaffolds for further study, which may facilitate the discovery and rational design of potent c-Met kinase inhibitors. FigureA reliable pharmacophore model was built based on known c-Met inhibitors. The well validated model was used to screen compound databases for novel c-Met inhibitors for further study.

Structure-based and shape-complemented pharmacophore modeling for the discovery of novel checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1), a member of the serine/threonine kinase family, is an attractive therapeutic target for anticancer combination therapy. A structure-based modeling approach complemented with shape components was pursued to develop a reliable pharmacophore model for ATP-competitive Chk1 inhibitors. Common chemical features of the pharmacophore model were derived by clustering multiple structure-based pharmacophore features from different Chk1-ligand complexes in comparable binding modes. The final model consisted of one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD), two hydrophobic (HY) features, several excluded volumes and shape constraints. In the validation study, this feature-shape query yielded an enrichment factor of 9.196 and performed fairly well at distinguishing active from inactive compounds, suggesting that the pharmacophore model can serve as a reliable tool for virtual screening to facilitate the discovery of novel Chk1 inhibitors. Besides, these pharmacophore features were assumed to be essential for Chk1 inhibitors, which might be useful for the identification of potential Chk1 inhibitors.

De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction

Vascular endothelial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as promising targets for novel anticancer agents. To achieve new potent inhibitors of KDR, we conducted molecular fragment replacement (MFR) studies for the understanding of 3D-QSAR modeling and the docking investigation of arylphthalazines and 2-((1H-Azol-1-yl)methyl)-N-arylbenzamides-based KDR inhibitors. Two favorable 3D-QSAR models (CoMFA with q2, 0.671; r2, 0.969; CoMSIA with q2, 0.608; r2, 0.936) have been developed to predict the biological activity of new compounds. The new molecular database generated by MFR was virtually screened using Glide (docking) and further evaluated with CoMFA prediction, protein–ligand interaction fingerprint (PLIF) and ADMET analysis. 44 N-(pyridin-4-ylmethyl)aniline derivatives as novel potential KDR inhibitors were finally obtained. In this paper, the work flow developed could be applied to de novo drug design and virtual screening potential KDR inhibitors, and use hit compounds to further optimize and design new potential KDR inhibitors.

Design, synthesis and biological evaluation of β-carboline derivatives as novel inhibitors targeting B-Raf kinase.

β-Carboline family of compounds is a large group of alkaloids widely distributed in nature and exhibits broad-spectrum anti-tumor activities. We designed and synthesized two series of novel 1-carboxamide- and 6-sulfonamide-substituted β-carboline derivatives 7a-p and 12a-b, and their wild type B-Raf kinase inhibitory activities were described. Most compounds showed moderate to excellent inhibitory activities. Among them, 1-carboxamide-6-(N-(3-(dimethylamino)propyl)-sulfamoyl)-β-carboline, 7e exhibited potent activity (IC(50)=1.62 μM), showing the potential for further investigation as a lead compound.

Combined Pharmacophore Modeling, Docking, and 3D-QSAR Studies of PLK1 Inhibitors

Polo-like kinase 1, an important enzyme with diverse biological actions in cell mitosis, is a promising target for developing novel anticancer drugs. A combined molecular docking, structure-based pharmacophore modeling and three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a set of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as PLK1 inhibitors. The common substructure, molecular docking and pharmacophore-based alignment were used to develop different 3D-QSAR models. The comparative molecular field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) models gave statistically significant results. These models showed good q2 and r2 pred values and revealed a good response to test set validation. All of the structural insights obtained from the 3D-QSAR contour maps are consistent with the available crystal structure of PLK1. The contour maps obtained from the 3D-QSAR models in combination with the structure based pharmacophore model help to better interpret the structure-activity relationship. These satisfactory results may aid the design of novel PLK1 inhibitors. This is the first report on 3D-QSAR study of PLK1 inhibitors.

A selectivity study on mTOR/PI3Kα inhibitors by homology modeling and 3D-QSAR

The phosphatidylinositol-3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) signaling pathway plays a critical role in the regulation of cellular growth, survival and proliferation. mTOR and PI3K have attracted particular attention as cancer targets. These kinases belong to the phosphatidylinositol-3-kinase-related kinase (PIKK) family and therefore have considerable homology in their active sites. To accelerate the discovery of inhibitors with selective activity against mTOR and PI3K as cancer targets, in this work, a homology model of mTOR was developed to identify the structural divergence in the active sites between mTOR and PI3Kα. Furthermore, two highly predictive comparative molecular similarity index analyses (CoMSIA) models were built based on 304 selective inhibitors docked into mTOR and PI3Kα, respectively (mTOR: q2 = 0.658, rpre2 = 0.839; PI3Kα: q2 = 0.540, rpre2 = 0.719). The results showed that steric and electrostatic fields have an important influence on selectivity towards mTOR and PI3Kα—a finding consistent with the structural divergence between the active sites. The findings may be helpful in investigating selective mTOR/PI3Kα inhibitors.

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.