Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis.

Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

Development of an in silico prediction model for chemical-induced urinary tract toxicity by using naïve Bayes classifier.

The urinary tract toxicity is one of the major reasons for investigational drugs not coming into the market and even marketed drugs being restricted or withdrawn. The objective of this investigation is to develop an easily interpretable and practically applicable in silico prediction model of chemical-induced urinary tract toxicity by using naïve Bayes classifier. The genetic algorithm was used to select important molecular descriptors related to urinary tract toxicity, and the ECFP-6 fingerprint descriptors were applied to the urinary tract toxic/non-toxic fragments production. The established naïve Bayes classifier (NB-2) produced 87.3% overall accuracy of fivefold cross-validation for the training set and 84.2% for the external test set, which can be employed for the chemical-induced urinary tract toxicity assessment. Furthermore, six important molecular descriptors (e.g., number of N atoms, AlogP, molecular weight, number of H acceptors, number of H donors and molecular fractional polar surface area) and toxic and non-toxic fragments were obtained, which would help medicinal chemists interpret the mechanisms of urinary tract toxicity, and even provide theoretical guidance for hit and lead optimization.

Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method.

Respiratory toxicity is considered as main cause of drug withdrawal, which could seriously injure human health or even lead to death. The objective of this investigation was to develop an in silico prediction model of drug-induced respiratory toxicity by using naïve Bayes classifier. The genetic algorithm was used to select important molecular descriptors related to respiratory toxicity, and the ECFP_6 fingerprint descriptors were applied to the respiratory toxic/non-toxic fragments production. The established prediction model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier generated overall prediction accuracy of 91.8% for the training set and 84.3% for the external test set. Furthermore, six molecular descriptors (e.g., number of O atoms, number of N atoms, molecular weight, Apol, number of H acceptors and molecular polar surface area) considered as important for the drug-induced respiratory toxicity were identified, and some critical fragments related to the respiratory toxicity were achieved. We hope the established naïve Bayes prediction model could be used as a toxicological screening of chemicals for respiratory sensitization potential in drug development, and these obtained important information of respiratory toxic chemical structures could offer theoretical guidance for hit and lead optimization.

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer.

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using 'Receptor-Ligand Pharmacophore Generation' method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.996; for the test set, the correlation coefficient r2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.

Development of novel prediction model for drug-induced mitochondrial toxicity by using naïve Bayes classifier method.

Mitochondrial dysfunction has been considered as an important contributing factor in the etiology of drug-induced organ toxicity, and even plays an important role in the pathogenesis of some diseases. The objective of this investigation was to develop a novel prediction model of drug-induced mitochondrial toxicity by using a naïve Bayes classifier. For comparison, the recursive partitioning classifier prediction model was also constructed. Among these methods, the prediction performance of naïve Bayes classifier established here showed best, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set were 95 ± 0.6% and 81 ± 1.1%, respectively. In addition, four important molecular descriptors and some representative substructures of toxicants produced by ECFP_6 fingerprints were identified. We hope the established naïve Bayes prediction model can be employed for the mitochondrial toxicity assessment, and these obtained important information of mitochondrial toxicants can provide guidance for medicinal chemists working in drug discovery and lead optimization.

Development of novel in silico model for developmental toxicity assessment by using naïve Bayes classifier method.

Toxicological testing associated with developmental toxicity endpoints are very expensive, time consuming and labor intensive. Thus, developing alternative approaches for developmental toxicity testing is an important and urgent task in the drug development filed. In this investigation, the naïve Bayes classifier was applied to develop a novel prediction model for developmental toxicity. The established prediction model was evaluated by the internal 5-fold cross validation and external test set. The overall prediction results for the internal 5-fold cross validation of the training set and external test set were 96.6% and 82.8%, respectively. In addition, four simple descriptors and some representative substructures of developmental toxicants were identified. Thus, we hope the established in silico prediction model could be used as alternative method for toxicological assessment. And these obtained molecular information could afford a deeper understanding on the developmental toxicants, and provide guidance for medicinal chemists working in drug discovery and lead optimization.

Identification of novel dual-specificity phosphatase 26 inhibitors by a hybrid virtual screening approach based on pharmacophore and molecular docking.

Dual-specificity phosphatase 26 (DUSP26) has recently emerged as a target for treatment of human cancers. However, only two small-molecule inhibitors of DUSP26 are known so far, namely NSC-87877 and ethyl-3, 4-dephostatin. DUSP26 contains an N-terminal region (residues 1-60) and a conserved C-terminal catalytic domain (residues 61-211, DUSP26-C). The crystal structure of DUSP26-C, showing a catalytically inactive conformation of the active site, was reported in a previous study. However, the detailed catalytic mechanism of DUSP26 cannot be described based on that structure. In this study, the 3D structure of DUSP26 (residues 42-211) adopting catalytically active conformation, was built by homology modeling, and the established 3D structure was validated using enzyme kinetic assays. Pharmacophore modeling based on the validated 3D structure of human DUSP26 was carried out. The established pharmacophore model was considered as a 3D query for retrieving novel DUSP26 inhibitors from the chemical databases "Diversity Libraries" (129,087 compounds). Next, a docking study was performed to refine the obtained hit compounds. Then a total of 100 compounds were selected based on the ranking order and visual examination, which were then evaluated by an enzyme-based assay. Eight compounds were found to have inhibitory activities against DUSP26, and the most potent compound was assigned No. F1063-0967 with an IC50 value of 11.62µM. The inhibitory activity of F1063-0967 against DUSP26 is higher than that of NCS87877 (IC50 value: 16.67±2.89µM), but lower than that of ethyl-3, 4-dephostatin (IC50 value: 6.8±0.41µM). MTT assay results revealed that F1063-0967 can induce apoptosis in IMR-32 cell line with an IC50 value of 4.13µM. These results suggest that F1063-0967 should be investigated further for other pharmacological properties.

Identification of novel VP35 inhibitors: Virtual screening driven new scaffolds.

Ebola virus is a single-stranded, negative-sense RNA virus that causes acute and serious life-threatening illness. In recent years the Ebola virus has spread through several countries in Africa, highlighting the need to develop new treatments for this disease and boosting a new research effort on this subject. However, so far there is no valid treatment for disease created by this pathogen. The Ebola virus Viral Protein 35 (VP35) is a multifunctional protein which is critical for virus replication and infection, and it is considered as a future target for drug development. In this study, we collected 144 VP35 inhibitors which shared the same core scaffold, and a common feature pharmacophore model HypoA was built based on inhibitor-receptor complexes. All 141 compounds were aligned based on the common feature pharmacophore model HypoA (three compounds could not map onto HypoA). The pharmacophore model HypoA was further optimized according to the actual interactions between inhibitors and VP35 protein, resulting in a new pharmacophore model HypoB which was applied for virtual screening. A 3D QSAR model was established by applying the 141 aligned compounds. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.897, for the test set, the correlation coefficient r2 was 0.757. Then a virtual screening was carried out, which comprehensively employing the common feature pharmacophore model, 3D QSAR model and docking study, their combination in a hybrid protocol could help to mutually compensate for their limitations and capitalized on their mutual strengths. After the above three virtual screening methods orderly filtering, seven potential inhibitors with novel scaffolds were identified as new VP35 inhibitors. The mapping results of hit compounds onto pharmacophore model and 3D QSAR model, and the molecular interactions of the potential inhibitors with the active site residues have been discussed in detail.

Homology modeling and virtual screening for inhibitors of lipid kinase PI(4)K from Plasmodium.

Malaria parasite strains have emerged to tolerate the therapeutic effects of the prophylactics and drugs presently available. Recent studies have shown that KAI715 and its analogs inhibit malaria parasites growth by binding to lipid kinase PI(4)K (phosphatidylinositol-4-OH kinase) of the parasites. Therefore, targeting PI(4)K may open up new avenues of target-based drug discovery to identify novel anti-malaria drugs. In this investigation, we describe the discovery of novel potent PfPI(4)K (PI(4)K from P. falciparum) inhibitors by employing a proposed hybrid virtual screening (VS) method, including pharmacophore model, drug-likeness prediction and molecular docking approach. 3D structure of PfPI(4)K has been established by homology modeling. Pharmacophore model HypoA of PfPI(4)K inhibitors has been developed based on the ligand complexed with its corresponding receptor. 174 compounds with good ADMET properties were carefully selected by a hybrid virtual screening method. Finally, the 174 hits were further validated by using a new pharmacophore model HypoB built based on the docking pose of BQR685, and 95 compounds passed the last filter. These compounds would be further evaluated by biological activity assays. The molecular interactions of the top two potential inhibitors with the active site residues are discussed in detail. These identified hits can be further used for designing the more potent inhibitors against PfPI(4)K by scaffold hopping, and deserve consideration for further structure-activity relationship (SAR) studies.

Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: inhibitors targeting RmlB and RmlC.

BACKGROUND: Tuberculosis remains one of the deadliest infectious diseases in humans. It has caused more than 100 million deaths since its discovery in 1882. Currently, more than 5 million people are infected with TB bacterium each year. The cell wall of Mycobacterium tuberculosis plays an important role in maintaining the ability of mycobacteria to survive in a hostile environment. Therefore, we report a virtual screening (VS) study aiming to identify novel inhibitors that simultaneously target RmlB and RmlC, which are two essential enzymes for the synthesis of the cell wall of M. tuberculosis. METHODS: A hybrid VS method that combines drug-likeness prediction, pharmacophore modeling and molecular docking studies was used to indentify inhibitors targeting RmlB and RmlC. RESULTS: The pharmacophore models HypoB and HypoC of RmlB inhibitors and RmlC inhibitors, respectively, were developed based on ligands complexing with their corresponding receptors. In total, 20 compounds with good absorption, distribution, metabolism, excretion, and toxicity properties were carefully selected using the hybird VS method. DISCUSSION: We have established a hybrid VS method to discover novel inhibitors with new scaffolds. The molecular interactions of the selected potential inhibitors with the active-site residues are discussed in detail. These compounds will be further evaluated using biological activity assays and deserve consideration for further structure-activity relationship studies.

Pharmacophore modeling and hybrid virtual screening for the discovery of novel IκB kinase 2 (IKK2) inhibitors.

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.

Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking.

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.

Pharmacophore modeling and virtual screening for the discovery of new transforming growth factor-beta type I receptor (ALK5) inhibitors.

Inhibitors of transforming growth factor-beta Type I Receptor (ALK5) have been thought as potential drugs for the treatment of fibrosis and cancer and a considerable number of ALK5 inhibitors have been reported recently. In order to clarify the essential structure-activity relationship for the known ALK5 inhibitors as well as identify new lead compounds against ALK5, 3D pharmacophore models have been established based on the known ALK5 inhibitors. The best pharmacophore model, Hypo1, was used as a 3D search query to perform a virtual screening. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking studies to refine the retrieved hits. Finally a total of 100 compounds were obtained and some of them were selected for further in vitro and in vivo assay studies.

Pharmacophore modeling study based on known spleen tyrosine kinase inhibitors together with virtual screening for identifying novel 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.

Pharmacophore modeling studies of type I and type II kinase inhibitors of Tie2.

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.

Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2.

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.