Assessment of a Rule-Based Virtual Screening Technology (INDDEx) on a Benchmark Data Set

Ensemble docking-based virtual screening toward identifying inhibitors against Wee1 kinase

Aim: Wee1 kinase plays a key role in the arrest of G2/M checkpoint that prevents mitotic entry in response to DNA damage. This work is to discover potent Wee1 inhibitors which can be considered valuable. Materials & Methods: Herein, Ensemble docking using multiple crystal structures was considered an effective strategy in the virtual screening. The performance of 17 scoring functions obtained from different docking software was evaluated for molecular docking. Results: Two novel compounds B1 and A2 were identified as Wee1 inhibitors with IC₅₀ values of 10.23 ± 0.505 and 8.72 ± 0.323 μM, respectively. Further cell viability assay demonstrated that the two active compounds exhibited good anticancer activities. Conclusion: This provides a meaningful starting point for further structure optimization to discover more potent Wee1 inhibitors.

GCAC: galaxy workflow system for predictive model building for virtual screening

Background

Traditional drug discovery approaches are time-consuming, tedious and expensive. Identifying a potential drug-like molecule using high throughput screening (HTS) with high confidence is always a challenging task in drug discovery and cheminformatics. A small percentage of molecules that pass the clinical trial phases receives FDA approval. This whole process takes 10–12 years and millions of dollar of investment. The inconsistency in HTS is also a challenge for reproducible results. Reproducible research in computational research is highly desirable as a measure to evaluate scientific claims and published findings. This paper describes the development and availability of a knowledge based predictive model building system using the R Statistical Computing Environment and its ensured reproducibility using Galaxy workflow system.

Results

We describe a web-enabled data mining analysis pipeline which employs reproducible research approaches to confront the issue of availability of tools in high throughput virtual screening. The pipeline, named as “Galaxy for Compound Activity Classification (GCAC)” includes descriptor calculation, feature selection, model building, and screening to extract potent candidates, by leveraging the combined capabilities of R statistical packages and literate programming tools contained within a workflow system environment with automated configuration.

Conclusion

GCAC can serve as a standard for screening drug candidates using predictive model building under galaxy environment, allowing for easy installation and reproducibility. A demo site of the tool is available at http://ccbb.jnu.ac.in/gcac

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2492-8) contains supplementary material, which is available to authorized users.

Incorporating Virtual Reactions into a Logic-based Ligand-based Virtual Screening Method to Discover New Leads

The use of virtual screening has become increasingly central to the drug development pipeline, with ligand-based virtual screening used to screen databases of compounds to predict their bioactivity against a target. These databases can only represent a small fraction of chemical space, and this paper describes a method of exploring synthetic space by applying virtual reactions to promising compounds within a database, and generating focussed libraries of predicted derivatives. A ligand-based virtual screening tool Investigational Novel Drug Discovery by Example (INDDEx) is used as the basis for a system of virtual reactions. The use of virtual reactions is estimated to open up a potential space of 1.21×1012 potential molecules. A de novo design algorithm known as Partial Logical-Rule Reactant Selection (PLoRRS) is introduced and incorporated into the INDDEx methodology. PLoRRS uses logical rules from the INDDEx model to select reactants for the de novo generation of potentially active products. The PLoRRS method is found to increase significantly the likelihood of retrieving molecules similar to known actives with a p-value of 0.016. Case studies demonstrate that the virtual reactions produce molecules highly similar to known actives, including known blockbuster drugs.

Enrichment assessment of multiple virtual screening strategies for Toll-like receptor 8 agonists based on a maximal unbiased benchmarking data set

Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed 'mubd-decoymaker' protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

The discovery of a highly selective 5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidin-4(3H)-one SIRT2 inhibitor that is neuroprotective in an in vitro Parkinson's disease model

Sirtuins, NAD(+) -dependent histone deacetylases (HDACs), have recently emerged as potential therapeutic targets for the treatment of a variety of diseases. The discovery of potent and isoform-selective inhibitors of this enzyme family should provide chemical tools to help determine the roles of these targets and validate their therapeutic value. Herein, we report the discovery of a novel class of highly selective SIRT2 inhibitors, identified by pharmacophore screening. We report the identification and validation of 3-((2-methoxynaphthalen-1-yl)methyl)-7-((pyridin-3-ylmethyl)amino)-5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidin-4(3H)-one (ICL-SIRT078), a substrate-competitive SIRT2 inhibitor with a Ki value of 0.62 ± 0.15 μM and more than 50-fold selectivity against SIRT1, 3 and 5. Treatment of MCF-7 breast cancer cells with ICL-SIRT078 results in hyperacetylation of α-tubulin, an established SIRT2 biomarker, at doses comparable with the biochemical IC50 data, while suppressing MCF-7 proliferation at higher concentrations. In concordance with the recent reports that suggest SIRT2 inhibition is a potential strategy for the treatment of Parkinson's disease, we find that compound ICL-SIRT078 has a significant neuroprotective effect in a lactacystin-induced model of Parkinsonian neuronal cell death in the N27 cell line. These results encourage further investigation into the effects of ICL-SIRT078, or an optimised derivative thereof, as a candidate neuroprotective agent in in vivo models of Parkinson's disease.