A data warehouse as an infrastructure to mine molecular descriptors for virtual screening

With the emergence of SARS-CoV-2 mutant strains, especially the epidemic of Omicron, it continues to evolve to strengthen immune evasion. Omicron BQ. 1 and XBB pose a serious threat to the current COVID-19 vaccine (including bivalent mRNA vaccine for mutant strains) and COVID-19-positive survivors, and all current therapeutic monoclonal antibodies are ineffective against them. Older people, those with multimorbidity, and those with specific underlying health conditions remain at increased risk of COVID-19 hospitalization and death after the initial vaccine booster. However, small-molecule drugs for conserved targets remain effective and urgently needed. The non-structural protein of SARS-CoV-2 non-structural protein 1(Nsp1) can bind to the host 40S ribosomal subunit and activate the nuclease to hydrolyze the host RNA, while the viral RNA is unaffected, thus hijacking the host system. First, the present study analyzed mutations in the Nsp1 protein and then constructed a maximum-likelihood phylogenetic tree. A virtual drug screening method based on the Nsp1 structure (Protein Data Bank ID: 7K5I) was constructed, 7495 compounds from three databases were collected for molecular docking and virtual screening, and the binding free energy was calculated by the MM/GBSA method. Our study shows that Nsp1 is relatively conserved and can be used as a comparatively fixed drug target and that therapies against Nsp1 will target all of these variants. Golvatinib, Gliquidone, and Dihydroergotamine were superior to other compounds in the crystal structure of binding conformation and free energy. All effectively interfered with Nsp1 binding to 40S protein, confirming the potential inhibitory effect of these three compounds on SARS-CoV-2. In particular, Golwatinib provides a candidate for treatment and prophylaxis in elderly patients with Omicjon, suggesting further evaluation of the anti-SARS-CoV-2 activity of these compounds in cell culture. Further studies are needed to determine the utility of this finding through prospective clinical trials and identify other meaningful drug combinations.

Virtual screening of small molecule databases against macromolecular targets was used to identify binding ligands and predict their lowest energy bound conformation (i.e., pose). AutoDock4-generated poses were rescored using mean-field pathway decoupling free energy of binding calculations and evaluated if these calculations improved virtual screening discrimination between bound and nonbound ligands. Two small molecule databases were used to evaluate the effectiveness of the rescoring algorithm in correctly identifying binders of L99A T4 lysozyme. Self-dock calculations of a database containing compounds with known binding free energies and cocrystal structures largely reproduced experimental measurements, although the mean difference between calculated and experimental binding free energies increased as the predicted bound poses diverged from the experimental poses. In addition, free energy rescoring was more accurate than AutoDock4 scores in discriminating between known binders and nonbinders, suggesting free energy rescoring could be a useful approach to reduce false positive predictions in virtual screening experiments.

The combination of chromatography and chemometrics has been introduced in order to provide information about drug analytes, biological macromolecules, the stationary phase, whose properties are related to molecular pharmacology and rational drug design. In this work a pharmacological classification was made in order to find strategy for predicting an activity of anticancer drugs. Principal Component Analysis (PCA) method has been employed to build some relationship models between the following: lipophilicity parameters of selected antitumor drugs obtained by chromatographic analysis, some molecular modeling descriptors from HyperChem software, lipophilicity parameters calculated by proper program, biological activity, as well as mechanism of actions and therapeutic properties of selected anticancer drugs. The most significant influence on factors value had parameters, which described compounds’ lipophilicity. Important debt on antitumor properties had some other molecular descriptors, which were considered in this paper (e.g. topological, physico-chemical, and energy ones). In addition, distribution of individual drugs on plots determined by two principal components had shown points in good accordance with their chemical structures and cytostatic activity as well as their mechanism of actions and therapeutic properties.

Background: NIMA-related kinase 7 (NEK7) is a serine/threonine kinase that is required for cell cycle progression, particularly mitotic spindle formation and cytokinesis, and has been linked to various cancers. Furthermore, NEK7 modulates the NLRP3 inflammasome, which activates Caspase-1 and causes cell pyroptosis. A growing body of studies indicates that ginger has the ability to prevent and treat a variety of health issues.Methods: In this study, 383 distinct bioactive compounds were extracted from the LOTUS database from Zingiber officinale and were screened against NEK7 protein using the PyRx 0.8 software. The top six hits' physicochemical characteristics, molecular descriptors, and natural product-likeness ratings were assessed using the LOTUS database.Results: The compounds LTS0101379, LTS0022317, TS0094590, LTS0231343, LTS0085481, and LTS0110340 showed high affinity for NEK7, forming interactions with critical amino acid residues. Notably, these compounds had multiple interactions with amino acid residues similar to those of the control compound Dabrafenib. Furthermore, the molecular properties and descriptors of these compounds indicated favorable drug-like properties.Conclusion: These findings highlight the potential of these compounds as NEK7 inhibitors for cancer management, though additional experimental validation is required.Keywords: NEK7; Cancer; Zingiber officinale; Virtual screening; Drug-likeness

Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. Much of the interest and success in this area can be attributed to the need for software and tools to improve data management and analysis given the large amounts of information that are being accumulated in corporate as well as scientific databases.However, even though the high maturity of these technologies, new data needs or applications currently run at companies not only demand more capacity, but also new methods, models, techniques or architectures to satisfy these new needs. Some of the hot topics in data warehouses (DWs) include distributed DWs, advanced OLAP for business intelligence, web warehouses, DWs for new applications such as XML documents, stream data, spatial or GIS data or biomedical data. Moreover, there are other aspects very developed in other software areas such as security or quality, which still remain uncovered by current design methods or technologies for DWs.Like the previous successful DOLAP workshops held in conjunction with CIKMs, the eighth edition of the Workshop on Data Warehousing and OLAP (DOLAP'05) aims to synergistically connect the research community and industry practitioners. It provides an international forum where both researchers and practitioners can share their findings in theoretical foundations, current methodologies, and practical experiences. This year, DOLAP'05 will be specially focused on new research directions and emerging application domains in the areas of data warehousing and OLAP.This year, we received papers from 18 different countries distributed over all continents such as The Netherlands, France, Spain, Israel, Korea, USA, Canada and Argentine. We received 31 submissions and, after a careful review, only 12 papers were selected by the Program Committee, making an acceptance rate of 38.7%.These proceedings contain the papers selected for presentation at the workshop. The accepted papers were presented in 5 sessions: (i) querying OLAP databases, (ii) data warehouse models, (iii, iv) data warehouse design, and (v) query processing and view maintenance. A keynote address was given by Jens Lechtenburger on Schema transformations. We hope that these proceedings will serve as a valuable reference for data warehousing and OLAP researchers and practitioners.

QSAR for dihydrofolate reductase inhibitors with molecular graph structural descriptors

The two driving forces for designing dimensional models of data warehouse are data sources and business requirements. Medical data sources are characterized by too many data types, large amount of data, lack of associations among data, mixed dictionary tables, etc., which result in lacking uniform representation and effective organization among a large number of concepts. These features may lead to serious semantic heterogeneity among different data sources. Medical data warehouse design involves a wide range of business requirements, while the users' perspectives are different and the expression of semantic heterogeneity is substantial. So how to obtain and integrate these business requirements is a challenge. Aiming at the characteristics of medical data sources and business requirements, this paper proposes an ontology-based medical data warehouse dimensional modeling method. Experiment shows that this method can not only optimize the data warehouse requirements analysis process, but also effectively eliminate the semantic heterogeneity in data sources and business requirements, thereby greatly improving the efficiency of medical data warehouse dimensional modeling.

The 2019 coronavirus (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has significantly impacted human lives, overburdened the healthcare system, and weakened global economies. The lack of specific drugs against SARS-CoV-2 is a significant hurdle toward the successful treatment of COVID-19. The SARS-CoV-2 Main protease (Mpro) is considered an appealing target because of its role in replication in host cells. Plant-derived natural compounds are being largely tested for their efficacy against COVID-19 targets to combat SARS-CoV-2 infection. To discover hit compounds that can be used alone or in combination with repositioned drugs, we curated a set of 224,205 natural product structures from the ZINC database and virtually screened it against COVID-19 Mpro. Sequential docking protocols involving different levels of exhaustiveness were performed to screen a library of natural compounds. The final 88 compounds were selected and post-processed using the MM-GBSA analysis for the generation of binding free energies. The top four compounds (ZINC000085626103, ZINC000085569275, ZINC000085625768, and ZINC000085488571) showed higher affinity against the COVID-19 Mpro enzyme selected for MD simulation studies. The RMSD, RMSF, and RoG analysis of all four compound–protein complexes indicated absolute stability during a 100 ns MD run. Furthermore, the post-MD simulation binding free energies were calculated for all four compounds and were found to be in the range of −38.29 to −18.07 kcal/mol. The in silico virtual screening results suggested that the selected natural compounds have the potential to be developed as a COVID-19 Mpro inhibitor and can be explored further for experimental research to evaluate the in vitro and in vivo efficacy of these compounds for the treatment of COVID-19.

This chapter presents an approach for designing secure Data Warehouses (DWs) that accomplish the conceptual modeling of secure DWs independently from the target platform where the DW has to be implemented, because our complete approach follows the Model Driven Architecture (MDA) and the Model Driven Security (MDS). In most of real world DW projects, the security aspects are issues that usually rely on the DBMS administrators. We argue that the design of these security aspects should be considered together with the conceptual modeling of DWs from the early stages of a DW project, and being able to attach user security information to the basic structures of a Multidimensional (MD) model. In this way, we would be able to generate this information in a semi or automatic way into a target platform and the final DW will better suits the user security requirements.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. Here, we present a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening.

In the past few years, several conceptual approaches have been proposed for the specification of the main multidimensional (MD) properties of the data warehouse (DW) repository. However, most of them deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW life cycle (ETL processes, data sources, DW repository and so on).Some approaches are depending on specific platform or neglecting important issues in DW design life cycle. Extraction-transformation-loading (ETL) processes play an important role in data warehouse architecture because they are responsible of integrating data from heterogeneous data sources into the DW repository. This paper proposes a conceptual model to refresh data warehouse by (insert, update, delete) data using ETL processes and considering DW security requirements. Firstly, the proposed ETL model is based on Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of ETL processes .secondly; this part focuses on how to integrate the proposed ETL model with the DW model. The proposed DW model depends on the Model Driven Architecture (MDA). MDA is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. This paper proposes an integrated conceptual model for addressing temporal data warehouse security requirements (CMTDWS).

Virtual Screening of Some Active Human Macrophage Migration Inhibitory Factor Antagonists

Traditional data warehouses are manually designed starting from specific requirements and anticipated data analysis needs. As a result there is frequently a disconnect between business process models, business definition of data artifacts and the data stored in the data warehouses as they are often designed manually and in isolation. Hence it has always been a challenge to keep the data warehouse in sync with the continuously changing business process models, resulting in both high maintenance costs and lost opportunities. In this paper we present our Model Driven Data Warehousing (MDDW) approach in the area of Business Performance Management (BPM). The purpose of MDDW is to bridge the gap between the business process models and the data warehouse models, thus enable the rapid adaptation to changes in the business environment. We describe our modeling framework comprising the various modeling elements and meta-models that capture both business and IT data artifacts.

This paper describes implementation of business intelligence tools in the libraries. A complete procedure for building a data warehouse is described on the case study of the BISIS library management system. During development of a data warehouse model, user requirements about reporting are detected and structure of already existing transactional databases in the BISIS system is analysed. Based on this analysis, three data warehouse models have been proposed that would satisfy the requirements for analytical processing of data. The paper presents the usage of one OLAP tool, but the proposed data warehouse model is independent of the choice of OLAP tools and any other tool can be integrated with the proposed data warehouse.