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Microtubules play crucial role in process of mitosis and cell proliferation, which have been considered as attractive drug targets for anticancer therapy. The aim of this study was to discover novel and chemically diverse tubulin inhibitors for treatment of cancer. In this investigation, the multilayer virtual screening methods, including common feature pharmacophore model, structure-based pharmacophore model and molecular docking, were developed to screen BioDiversity database with 30,000 compounds. A total of 102 compounds were obtained by the virtual screening, and further filtered by diverse chemical clusters with desired properties and PAINS analysis. Finally, 50 compounds were selected and submitted to the biological evaluation. Among these hits, hits 8 and 30 with novel scaffolds displayed stronger antiproliferative activity on four human tumor cells including Hela, A549, MCF-7, and HepG2. Moreover, the two hits were subsequently submitted to molecular dynamic simulations of 90 ns with the aim of exploring the stability of ligand-protein interactions into the binding pocket, and further probing the mechanism of the interaction between tubulin and hits. The molecular dynamic simulation results revealed there had stronger interactions between tubulin and hits in equilibrium state. Therefore, the hits 8 and 30 have been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.

Development of reliable and efficient alternative in vivo methods for evaluation of the chemicals with potential neurotoxicity is an urgent need in the early stages of drug design. In this investigation, the computational prediction models for drug-induced neurotoxicity were developed by using the classical naïve Bayes classifier. Eight molecular properties closely relevant to neurotoxicity were selected. Then, 110 classification models were developed with using the eight important molecular descriptors and 10 types of fingerprints with 11 different maximum diameters. Among these 110 prediction models, the prediction model (NB-03) based on eight molecular descriptors combined with ECFP_10 fingerprints showed the best prediction performance, which gave 90.5% overall prediction accuracy for the training set and 82.1% concordance for the external test set. In addition, compared to naïve Bayes classifier, the recursive partitioning classifier displayed worse predictive performance for neurotoxicity. Therefore, the established NB-03 prediction model can be used as a reliable virtual screening tool to predict neurotoxicity in the early stages of drug design. Moreover, some structure alerts for characterizing neurotoxicity were identified in this research, which could give an important guidance for the chemists in structural modification and optimization to reduce the chemicals with potential neurotoxicity.

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery.

In this investigation, a common feature pharmacophore model of anaplastic lymphoma kinase inhibitors was developed based on several known anaplastic lymphoma kinase inhibitors that were co-crystallized with anaplastic lymphoma kinase. The established pharmacophore model Hypo1 was carefully validated and then adopted to screen two in silico chemical databases, Specs (202 408 compounds) and Enamine (1 105 894 compounds), for retrieving novel anaplastic lymphoma kinase inhibitors. The hit compounds were further filtered using a fast bumping-check tool and molecular docking. Finally, 25 compounds were selected and purchased from market. The bioactivity of these compounds was firstly measured at the cellular level against a typical anaplastic lymphoma kinase mutant-driven cancer cell line, Karpas299. And six of them showed a good anti-viability activity. The kinase inhibitory potency against the recombinant human anaplastic lymphoma kinase kinase was tested to the most active compound at the cellular level, T0508-5181 (from Specs), which gave a half maximal inhibitory concentration (IC(50)) of 5.3 μM.

Protein kinase casein kinase 2 (CK2), a member of the serine/threonine kinase family, has been established as one of the most attractive targets for molecularly targeted cancer therapy. The discovery of CK2 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening approach based on Bayesian classification model, pharmacophore hypothesis and molecular docking was proposed and employed to identify CK2 inhibitors. We first established a naïve Bayes classification model of CK2 inhibitors/non-inhibitors and pharmacophore hypotheses of CK2 inhibitors. The docking parameters and scoring functions were also optimized in advance. The three virtual screening methods were sequentially used to screen two large chemical libraries, Specs and Enamine, for retrieving new CK2 inhibitors. Finally 30 compounds were selected from the final hits for in vitro CK2 kinase inhibitory assays. Five compounds with completely novel scaffolds showed a good inhibitory potency against CK2, which have good potentials for a future hit-to-lead optimization.

SKLB1028, a novel oral multikinase inhibitor of EGFR, FLT3 and Abl, displays exceptional activity in models of FLT3-driven AML and considerable potency in models of CML harboring Abl mutants

IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo81docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.

In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.

In this investigation, chemical features based 3D pharmacophore models were developed based on the known inhibitors of Spleen tyrosine kinase (Syk) with the aid of hiphop and hyporefine modules within catalyst. The best quantitative pharmacophore model, Hypo1, was used as a 3D structural query for retrieving potential inhibitors from chemical databases including Specs, NCI, MayBridge, and Chinese Nature Product Database (CNPD). The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking studies to refine the retrieved hits. Finally 30 compounds were selected from the top ranked hit compounds and conducted an in vitro kinase inhibitory assay. Six compounds showed a good inhibitory potency against Syk, which have been selected for further investigation.

In this study, chemical feature based pharmacophore models of type I and type II kinase inhibitors of Tie2 have been developed with the aid of HipHop and HypoRefine modules within Catalyst program package. The best HipHop pharmacophore model Hypo1_I for type I kinase inhibitors contains one hydrogen-bond acceptor, one hydrogen-bond donor, one general hydrophobic, one hydrophobic aromatic, and one ring aromatic feature. And the best HypoRefine model Hypo1_II for type II kinase inhibitors, which was characterized by the best correlation coefficient (0.976032) and the lowest RMSD (0.74204), consists of two hydrogen-bond donors, one hydrophobic aromatic, and two general hydrophobic features, as well as two excluded volumes. These pharmacophore models have been validated by using either or both test set and cross validation methods, which shows that both the Hypo1_I and Hypo1_II have a good predictive ability. The space arrangements of the pharmacophore features in Hypo1_II are consistent with the locations of the three portions making up a typical type II kinase inhibitor, namely, the portion occupying the ATP binding region (ATP-binding-region portion, AP), that occupying the hydrophobic region (hydrophobic-region portion, HP), and that linking AP and HP (bridge portion, BP). Our study also reveals that the ATP-binding-region portion of the type II kinase inhibitors plays an important role to the bioactivity of the type II kinase inhibitors. Structural modifications on this portion should be helpful to further improve the inhibitory potency of type II kinase inhibitors.

Aberrant c-Met activation has been demonstrated to be implicated in tumorigenesis and anti-cancer drug resistance. Discovery of c-Met inhibitors has attracted much attention in recent years. In this study, a support vector machine (SVM) classification model that discriminates c-Met inhibitors and non-inhibitors was first developed. Evaluation through screening a test set indicates that combined SVM-based and docking-based virtual screening (SB/DB-VS) considerably increases hit rate and enrichment factor compared with the individual methods. Thus the combined SB/DB-VS approach was adopted to screen PubChem, Specs, and Enamine for c-Met inhibitors. 75 compounds were selected for in vitro assays. Eight compounds display a good inhibitory potency against c-Met. Five of them are found to have novel scaffolds, implying a good potential for further chemical modification.

Development of glutamate non-competitive antagonists of mGluR1 (Metabotropic glutamate receptor subtype 1) has increasingly attracted much attention in recent years due to their potential therapeutic application for various nervous disorders. Since there is no crystal structure reported for mGluR1, ligand-based virtual screening (VS) methods, typically pharmacophore-based VS (PB-VS), are often used for the discovery of mGluR1 antagonists. Nevertheless, PB-VS usually suffers a lower hit rate and enrichment factor. In this investigation, we established a multistep ligand-based VS approach that is based on a support vector machine (SVM) classification model and a pharmacophore model. Performance evaluation of these methods in virtual screening against a large independent test set, M-MDDR, show that the multistep VS approach significantly increases the hit rate and enrichment factor compared with the individual SB-VS and PB-VS methods. The multistep VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally a total of 20 compounds were selected from the top ranking compounds, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future.

This investigation is to explore the feasibility of applying reverse docking method to the selectivity studies of protein kinase inhibitors. Firstly, a database that consists of 422 protein kinase structures was established through collecting the reported crystal structures or homology modeling. Then a reverse docking based method of protein kinase target screening was established, followed by the optimization of related parameters and scoring functions. Finally, seven typical selective kinase inhibitors were used to test the established method. The results show that the selective targets of these inhibitors have relatively high scoring function values (ranking in the first 35% of the tested kinase targets according to the scoring function values). This implies that the reverse docking method can be applied to the virtual screening of kinase targets and further to the selectivity studies of protein kinase inhibitors.

Pharmacophore modeling, including ligand- and structure-based approaches, has become an important tool in drug discovery. However, the ligand-based method often strongly depends on the training set selection, and the structure-based pharmacophore model is usually created based on apo structures or a single protein-ligand complex, which might miss some important information. In this study, multicomplex-based method has been suggested to generate a comprehensive pharmacophore map of cyclin-dependent kinase 2 (CDK2) based on a collection of 124 crystal structures of human CDK2-inhibitor complex. Our multicomplex-based comprehensive pharmacophore map contains almost all the chemical features important for CDK2-inhibitor interactions. A comparison with previously reported ligand-based pharmacophores has revealed that the ligand-based models are just a subset of our comprehensive map. Furthermore, one most-frequent-feature pharmacophore model consisting of the most frequent pharmacophore features was constructed based on the statistical frequency information provided by the comprehensive map. Validations to the most-frequent-feature model show that it can not only successfully discriminate between known CDK2 inhibitors and the molecules of focused inactive dataset, but also is capable of correctly predicting the activities of a wide variety of CDK2 inhibitors in an external active dataset. Obviously, this investigation provides some new ideas about how to develop a multicomplex-based pharmacophore model that can be used in virtual screening to discover novel potential lead compounds.