WENDI: A tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications

<p>Abstract</p> <p>Background</p> <p>In recent years, there has been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery. However, there is a distinct lack of data mining tools available to harness this information, and in particular for knowledge discovery across multiple information sources. At Indiana University we have an ongoing project with Eli Lilly to develop web-service based tools for integrative mining of chemical and biological information. In this paper, we report on the first of these tools, called WENDI (Web Engine for Non-obvious Drug Information) that attempts to find non-obvious relationships between a query compound and scholarly publications, biological properties, genes and diseases using multiple information sources.</p> <p>Results</p> <p>We have created an aggregate web service that takes a query compound as input, calls multiple web services for computation and database search, and returns an XML file that aggregates this information. We have also developed a client application that provides an easy-to-use interface to this web service. Both the service and client are publicly available.</p> <p>Conclusions</p> <p>Initial testing indicates this tool is useful in identifying potential biological applications of compounds that are not obvious, and in identifying corroborating and conflicting information from multiple sources. We encourage feedback on the tool to help us refine it further. We are now developing further tools based on this model.</p

Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data

<p>Abstract</p> <p>Background</p> <p>Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited</p> <p>Results</p> <p>We have created a single repository called Chem2Bio2RDF by aggregating data from multiple chemogenomics repositories that is cross-linked into Bio2RDF and LODD. We have also created a linked-path generation tool to facilitate SPARQL query generation, and have created extended SPARQL functions to address specific chemical/biological search needs. We demonstrate the utility of Chem2Bio2RDF in investigating polypharmacology, identification of potential multiple pathway inhibitors, and the association of pathways with adverse drug reactions.</p> <p>Conclusions</p> <p>We have created a new semantic systems chemical biology resource, and have demonstrated its potential usefulness in specific examples of polypharmacology, multiple pathway inhibition and adverse drug reaction - pathway mapping. We have also demonstrated the usefulness of extending SPARQL with cheminformatics and bioinformatics functionality.</p

Investigating the correlations among the chemical structures, bioactivity profiles and molecular targets of small molecules

Motivation: Most of the previous data mining studies based on the NCI-60 dataset, due to its intrinsic cell-based nature, can hardly provide insights into the molecular targets for screened compounds. On the other hand, the abundant information of the compound–target associations in PubChem can offer extensive experimental evidence of molecular targets for tested compounds. Therefore, by taking advantages of the data from both public repositories, one may investigate the correlations between the bioactivity profiles of small molecules from the NCI-60 dataset (cellular level) and their patterns of interactions with relevant protein targets from PubChem (molecular level) simultaneously

Evaluation of molecular descriptors for antitumor drugs with respect to noncovalent binding to DNA and antiproliferative activity

34 pages, 6 additional files, 5 tables, 4 figures.[Background ] Small molecules that bind reversibly to DNA are among the antitumor drugs currently used in chemotherapy. In the pursuit of a more rational approach to cancer chemotherapy based upon these molecules, it is necessary to exploit the interdependency between DNA-binding affinity, sequence selectivity and cytotoxicity. For drugs binding noncovalently to DNA, it is worth exploring whether molecular descriptors, such as their molecular weight or the number of potential hydrogen acceptors/donors, can account for their DNA-binding affinity and cytotoxicity.[Results] Fifteen antitumor agents, which are in clinical use or being evaluated as part of the National Cancer Institute’s drug screening effort, were analyzed in silico to assess the contribution of various molecular descriptors to their DNA-binding affinity, and the capacity of the descriptors and DNA-binding constants for predicting cell cytotoxicity. Equations to predict drug-DNA binding constants and growth-inhibitory concentrations were obtained by multiple regression following rigorous statistical procedures.[Conclusions] For drugs binding reversibly to DNA, both their strength of binding and their cytoxicity are fairly predicted from molecular descriptors by using multiple regression methods. The equations derived may be useful for rational drug design. The results obtained agree with that compounds more active across the National Cancer Institute’s 60-cell line data set tend to have common structural features.Supported by a grant from the former Spanish Ministry of Education and Science (BFU2007-60998) and the FEDER program of the European Community.Peer reviewe

SMARTS Approach to Chemical Data Mining and Physicochemical Property Prediction.

The calculation of physicochemical and biological properties is essential in order to facilitate modern drug discovery. Chemical spaces dimensionalized by these descriptors have been used to scaffold-hop in order to discover new lead and drug-like molecules. Broadening the boundaries of structure based drug design, these molecules are expected to share the same physiological target and have similar efficacy, as do known drug molecules sharing the same region in chemical property space. In the past few decades physicochemical and ADMET (absorption, distribution, metabolism, elimination, and toxicity) property predictors have been the subject of increased focus in academia and the pharmaceutical industry. Due to the ever increasing attention given to data mining and property predictions, we first discuss the sources of experimental pKa values and current methodologies used for pKa prediction in proteins and small molecules. Of particular concern is an analysis of the scope, statistical validity, overall accuracy, and predictive power of these methods. The expressed concerns are not limited to predicting pKa, but apply to all empirical predictive methodologies. In a bottom-up approach, we explored the influence of freely generated SMARTS string representations of molecular fragments on chelation and cytotoxicity. Later investigations, involving the derivation of predictive models, use stepwise regression to determine the optimal pool of SMARTS strings having the greatest influence over the property of interest. By applying a unique scoring system to sets of highly generalized SMARTS strings, we have constructed well balanced regression trees with predictive accuracy exceeding that of many published and commercially available models for cytotoxicity, pKa, and aqueous solubility. The methodology is robust, extremely adaptable, and can handle any molecular dataset with experimental data. This story details our struggles of data gathering, curation, and the development of a machine learning methodology able to derive and validate highly accurate regression trees capable of extremely fast property predictions. Regression trees created by our method are well suited to calculate descriptors for large in silico molecular libraries, facilitating data mining of chemical spaces in search of new lead molecules in drug discovery.Ph.D.Medicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64627/1/adamclee_1.pd

Chemical Data Mining of the NCI Human Tumor Cell Line Database

The NCI Developmental Therapeutics Program Human Tumor cell line Dataset is a publicly available database that contains cellular assay screening data for over 40,000 compounds tested in sixty human tumor cell lines. The database also contains microarray assay gene expression data for the cell lines, and so it provides an excellent information resource particularly for testing data mining methods that bridge chemical, biological and genomic information. In this paper we describe a formal knowledge discovery approach to characterizing and data mining this set, and report the results of some of our initial experiments in mining the set from a chemoinformatics perspective

Development and application of distributed computing tools for virtual screening of large compound libraries

Im derzeitigen Drug Discovery Prozess ist die Identifikation eines neuen Targetproteins und dessen potenziellen Liganden langwierig, teuer und zeitintensiv. Die Verwendung von in silico Methoden gewinnt hier zunehmend an Bedeutung und hat sich als wertvolle Strategie zur Erkennung komplexer Zusammenhänge sowohl im Bereich der Struktur von Proteinen wie auch bei Bioaktivitäten erwiesen. Die zunehmende Nachfrage nach Rechenleistung im wissenschaftlichen Bereich sowie eine detaillierte Analyse der generierten Datenmengen benötigen innovative Strategien für die effiziente Verwendung von verteilten Computerressourcen, wie z.B. Computergrids. Diese Grids ergänzen bestehende Technologien um einen neuen Aspekt, indem sie heterogene Ressourcen zur Verfügung stellen und koordinieren. Diese Ressourcen beinhalten verschiedene Organisationen, Personen, Datenverarbeitung, Speicherungs- und Netzwerkeinrichtungen, sowie Daten, Wissen, Software und Arbeitsabläufe. Das Ziel dieser Arbeit war die Entwicklung einer universitätsweit anwendbaren Grid-Infrastruktur - UVieCo (University of Vienna Condor pool) -, welche für die Implementierung von akademisch frei verfügbaren struktur- und ligandenbasierten Drug Discovery Anwendungen verwendet werden kann. Firewall- und Sicherheitsprobleme wurden mittels eines virtuellen privaten Netzwerkes gelöst, wohingegen die Virtualisierung der Computerhardware über das CoLinux Konzept ermöglicht wurde. Dieses ermöglicht, dass unter Linux auszuführende Aufträge auf Windows Maschinen laufen können. Die Effektivität des Grids wurde durch Leistungsmessungen anhand sequenzieller und paralleler Aufgaben ermittelt. Als Anwendungsbeispiel wurde die Assoziation der Expression bzw. der Sensitivitätsprofile von ABC-Transportern mit den Aktivitätsprofilen von Antikrebswirkstoffen durch Data-Mining des NCI (National Cancer Institute) Datensatzes analysiert. Die dabei generierten Datensätze wurden für liganden-basierte Computermethoden wie Shape-Similarity und Klassifikationsalgorithmen mit dem Ziel verwendet, P-glycoprotein (P-gp) Substrate zu identifizieren und sie von Nichtsubstraten zu trennen. Beim Erstellen vorhersagekräftiger Klassifikationsmodelle konnte das Problem der extrem unausgeglichenen Klassenverteilung durch Verwendung der „Cost-Sensitive Bagging“ Methode gelöst werden. Applicability Domain Studien ergaben, dass unser Modell nicht nur die NCI Substanzen gut vorhersagen kann, sondern auch für wirkstoffähnliche Moleküle verwendet werden kann. Die entwickelten Modelle waren relativ einfach, aber doch präzise genug um für virtuelles Screening einer großen chemischen Bibliothek verwendet werden zu können. Dadurch könnten P-gp Substrate schon frühzeitig erkannt werden, was möglicherweise nützlich sein kann zur Entfernung von Substanzen mit schlechten ADMET-Eigenschaften bereits in einer frühen Phase der Arzneistoffentwicklung. Zusätzlich wurden Shape-Similarity und Self-organizing Map Techniken verwendet um neue Substanzen in einer hauseigenen sowie einer großen kommerziellen Datenbank zu identifizieren, die ähnlich zu selektiven Serotonin-Reuptake-Inhibitoren (SSRI) sind und Apoptose induzieren können. Die erhaltenen Treffer besitzen neue chemische Grundkörper und können als Startpunkte für Leitstruktur-Optimierung in Betracht gezogen werden. Die in dieser Arbeit beschriebenen Studien werden nützlich sein um eine verteilte Computerumgebung zu kreieren die vorhandene Ressourcen in einer Organisation nutzt, und die für verschiedene Anwendungen geeignet ist, wie etwa die effiziente Handhabung der Klassifizierung von unausgeglichenen Datensätzen, oder mehrstufiges virtuelles Screening.In the current drug discovery process, the identification of new target proteins and potential ligands is very tedious, expensive and time-consuming. Thus, use of in silico techniques is of utmost importance and proved to be a valuable strategy in detecting complex structural and bioactivity relationships. Increased demands of computational power for tremendous calculations in scientific fields and timely analysis of generated piles of data require innovative strategies for efficient utilization of distributed computing resources in the form of computational grids. Such grids add a new aspect to the emerging information technology paradigm by providing and coordinating the heterogeneous resources such as various organizations, people, computing, storage and networking facilities as well as data, knowledge, software and workflows. The aim of this study was to develop a university-wide applicable grid infrastructure, UVieCo (University of Vienna Condor pool) which can be used for implementation of standard structure- and ligand-based drug discovery applications using freely available academic software. Firewall and security issues were resolved with a virtual private network setup whereas virtualization of computer hardware was done using the CoLinux concept in a way to run Linux-executable jobs inside Windows machines. The effectiveness of the grid was assessed by performance measurement experiments using sequential and parallel tasks. Subsequently, the association of expression/sensitivity profiles of ABC transporters with activity profiles of anticancer compounds was analyzed by mining the data from NCI (National Cancer Institute). The datasets generated in this analysis were utilized with ligand-based computational methods such as shape similarity and classification algorithms to identify and separate P-gp substrates from non-substrates. While developing predictive classification models, the problem of imbalanced class distribution was proficiently addressed using the cost-sensitive bagging approach. Applicability domain experiment revealed that our model not only predicts NCI compounds well, but it can also be applied to drug-like molecules. The developed models were relatively simple but precise enough to be applicable for virtual screening of large chemical libraries for the early identification of P-gp substrates which can potentially be useful to remove compounds of poor ADMET properties in an early phase of drug discovery. Additionally, shape-similarity and self-organizing maps techniques were used to screen in-house as well as a large vendor database for identification of novel selective serotonin reuptake inhibitor (SSRI) like compounds to induce apoptosis. The retrieved hits possess novel chemical scaffolds and can be considered as a starting point for lead optimization studies. The work described in this thesis will be useful to create distributed computing environment using available resources within an organization and can be applied to various applications such as efficient handling of imbalanced data classification problems or multistep virtual screening approach

System-Level Modeling of Endothelial Permeability Pathway and High-throughput Data Analysis for Disease Biomarker Selection

Ph.DDOCTOR OF PHILOSOPH

Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents

Ph.DDOCTOR OF PHILOSOPH