Ting Ran

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.

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.

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.

Targeting epigenetic reader and eraser: Rational design, synthesis and in vitro evaluation of dimethylisoxazoles derivatives as BRD4/HDAC dual inhibitors.

The bromodomain protein module and histone deacetylase (HDAC), which recognize and remove acetylated lysine, respectively, have emerged as important epigenetic therapeutic targets in cancer treatments. Herein we presented a novel design approach for cancer drug development by combination of bromodomain and HDAC inhibitory activity in one molecule. The designed compounds were synthesized which showed inhibitory activity against bromodomain 4 and HDAC1. The representative dual bromodomain/HDAC inhibitors, compound 11 and 12, showed potent antiproliferative activities against human leukaemia cell line K562 and MV4-11 in cellular assays. This work may lay the foundation for developing dual bromodomain/HDAC inhibitors as potential anticancer therapeutics.

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.

Molecular modelling on small molecular CDK2 inhibitors: an integrated approach using a combination of molecular docking, 3D-QSAR and pharmacophore modelling

Cyclin-dependent kinase 2 (CDK2) has been identified as an important target for developing novel anticancer agents. Molecular docking, three-dimensional quantitative structure–activity relationship (3D-QSAR) and pharmacophore modelling were combined with the ultimate goal of studying the structure–activity relationship of CDK2 inhibitors. The comparative molecular similarity indices analysis (CoMSIA) model constructed based on a set of 3-aminopyrazole derivatives as CDK2 inhibitors gave statistically significant results (q 2 = 0.700; r 2 = 0.982). A HypoGen pharmacophore model, constructed using diverse CDK2 inhibitors, also showed significant statistics ( Cost = 61.483; RMSD = 0.53; Correlation coefficient = 0.98). The small residues and error values between the estimated and experimental activities of the training and test set compounds proved their strong capability of activity prediction. The structural insights obtained from these two models were consistent with each other. The pharmacophore model summarized the important pharmacophoric features required for protein–ligand binding. The 3D contour maps in combination with the comprehensive pharmacophoric features helped to better interpret the structure–activity relationship. The results will be beneficial for the discovery and design of novel CDK2 inhibitors. The simplicity of this approach provides expansion to its applicability in optimizing other classes of small molecular CDK2 inhibitors.

An efficient multistep ligand-based virtual screening approach for GPR40 agonists.

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein–ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.

Fragment-based strategy for structural optimization in combination with 3D-QSAR.

Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure–activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.

Quantitative structure–activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods

Bruton’s tyrosine kinase (BTK) plays a crucial role in B-cell activation and development, and has emerged as a new molecular target for the treatment of autoimmune diseases and B-cell malignancies. In this study, two- and three-dimensional quantitative structure–activity relationship (2D and 3D-QSAR) analyses were performed on a series of pyridine and pyrimidine-based BTK inhibitors by means of genetic algorithm optimized multivariate adaptive regression spline (GA-MARS) and comparative molecular similarity index analysis (CoMSIA) methods. Here, we propose a modified MARS algorithm to develop 2D-QSAR models. The top ranked models showed satisfactory statistical results (2D-QSAR: Q2 = 0.884, r2 = 0.929, r2pred = 0.878; 3D-QSAR: q2 = 0.616, r2 = 0.987, r2pred = 0.905). Key descriptors selected by 2D-QSAR were in good agreement with the conclusions of 3D-QSAR, and the 3D-CoMSIA contour maps facilitated interpretation of the structure–activity relationship. A new molecular database was generated by molecular fragment replacement (MFR) and further evaluated with GA-MARS and CoMSIA prediction. Twenty-five pyridine and pyrimidine derivatives as novel potential BTK inhibitors were finally selected for further study. These results also demonstrated that our method can be a very efficient tool for the discovery of novel potent BTK inhibitors.