Ideaconsult Ltd.

Sofia, Bulgaria

Ideaconsult Ltd.

Sofia, Bulgaria
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Jeliazkova N.,Ideaconsult Ltd | Jeliazkov V.,Ideaconsult Ltd
Current Topics in Medicinal Chemistry | Year: 2012

The Structure-Activity Relationships (SAR) landscape and activity cliffs concepts have their origins in medicinal chemistry and receptor-ligand interactions modelling. While intuitive, the definition of an activity cliff as a "pair of structurally similar compounds with large differences in potency" is commonly recognized as ambiguous. This paper proposes a new and efficient method for identifying activity cliffs and visualization of activity landscapes. The activity cliffs definition could be improved to reflect not the cliff steepness alone, but also the rate of the change of the steepness. The method requires explicitly setting similarity and activity difference thresholds, but provides means to explore multiple thresholds and to visualize in a single map how the thresholds affect the activity cliff identification. The identification of the activity cliffs is addressed by reformulating the problem as a statistical one, by introducing a probabilistic measure, namely, calculating the likelihood of a compound having large activity difference compared to other compounds, while being highly similar to them. The likelihood is effectively a quantification of a SAS Map with defined thresholds. Calculating the likelihood relies on four counts only, and does not require the pairwise matrix storage. This is a significant advantage, especially when processing large datasets. The method generates a list of individual compounds, ranked according to the likelihood of their involvement in the formation of activity cliffs, and goes beyond characterizing cliffs by structure pairs only. The visualisation is implemented by considering the activity plane fixed and analysing the irregularities of the similarity itself. It provides a convenient analogy to a topographic map and may help identifying the most appropriate similarity representation for each specific SAR space. The proposed method has been applied to several datasets, representing different biological activities. Finally, the method is implemented as part of an existing open source Ambit package and could be accessed via an OpenTox API compliant web service and via an interactive application, running within a modern, JavaScript enabled web browser. Combined with the functionalities already offered by the OpenTox framework, like data sharing and remote calculations, it could be a useful tool for exploring chemical landscapes online. © 2012 Bentham Science Publishers.

Jeliazkova N.,IdeaConsult Ltd.
Expert Opinion on Drug Metabolism and Toxicology | Year: 2012

Introduction: The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. Areas covered: The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. Expert opinion: The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access. © 2012 Informa UK, Ltd. All rights reserved.

PubMed | Karolinska Institutet, in silico toxicology Gmbh IST, University of La Coruña, National and Kapodistrian University of Athens and 6 more.
Type: | Journal: Beilstein journal of nanotechnology | Year: 2015

The NanoSafety Cluster, a cluster of projects funded by the European Commision, identified the need for a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs). Ontologies, open standards, and interoperable designs were envisioned to empower a harmonized approach to European research in nanotechnology. This setting provides a number of opportunities and challenges in the representation of nanomaterials data and the integration of ENM information originating from diverse systems. Within this cluster, eNanoMapper works towards supporting the collaborative safety assessment for ENMs by creating a modular and extensible infrastructure for data sharing, data analysis, and building computational toxicology models for ENMs.The eNanoMapper database solution builds on the previous experience of the consortium partners in supporting diverse data through flexible data storage, open source components and web services. We have recently described the design of the eNanoMapper prototype database along with a summary of challenges in the representation of ENM data and an extensive review of existing nano-related data models, databases, and nanomaterials-related entries in chemical and toxicogenomic databases. This paper continues with a focus on the database functionality exposed through its application programming interface (API), and its use in visualisation and modelling. Considering the preferred community practice of using spreadsheet templates, we developed a configurable spreadsheet parser facilitating user friendly data preparation and data upload. We further present a web application able to retrieve the experimental data via the API and analyze it with multiple data preprocessing and machine learning algorithms.We demonstrate how the eNanoMapper database is used to import and publish online ENM and assay data from several data sources, how the representational state transfer (REST) API enables building user friendly interfaces and graphical summaries of the data, and how these resources facilitate the modelling of reproducible quantitative structure-activity relationships for nanomaterials (NanoQSAR).

PubMed | IdeaConsult Ltd, Uppsala University, Copenhagen University and Maastricht University
Type: | Journal: Journal of cheminformatics | Year: 2016

Xenobiotic metabolism is an active research topic but the limited amount of openly available high-quality biotransformation data constrains predictive modeling. Current database often default to commonly available information: which enzyme metabolizes a compound, but neither experimental conditions nor the atoms that undergo metabolization are captured. We present XMetDB, an open access database for drugs and other xenobiotics and their respective metabolites. The database contains chemical structures of xenobiotic biotransformations with substrate atoms annotated as reaction centra, the resulting product formed, and the catalyzing enzyme, type of experiment, and literature references. Associated with the database is a web interface for the submission and retrieval of experimental metabolite data for drugs and other xenobiotics in various formats, and a web API for programmatic access is also available. The database is open for data deposition, and a curation scheme is in place for quality control. An extensive guide on how to enter experimental data into is available from the XMetDB wiki. XMetDB formalizes how biotransformation data should be reported, and the openly available systematically labeled data is a big step forward towards better models for predictive metabolism.

PubMed | European Bioinformatics Institute, National Technical University of Athens, IdeaConsult Ltd. and Maastricht University
Type: | Journal: Journal of biomedical semantics | Year: 2015

Engineered nanomaterials (ENMs) are being developed to meet specific application needs in diverse domains across the engineering and biomedical sciences (e.g. drug delivery). However, accompanying the exciting proliferation of novel nanomaterials is a challenging race to understand and predict their possibly detrimental effects on human health and the environment. The eNanoMapper project ( is creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. Here, we describe the development of the eNanoMapper ontology based on adopting and extending existing ontologies of relevance for the nanosafety domain. The resulting eNanoMapper ontology is available at We aim to make the re-use of external ontology content seamless and thus we have developed a library to automate the extraction of subsets of ontology content and the assembly of the subsets into an integrated whole. The library is available (open source) at Finally, we give a comprehensive survey of the domain content and identify gap areas. ENM safety is at the boundary between engineering and the life sciences, and at the boundary between molecular granularity and bulk granularity. This creates challenges for the definition of key entities in the domain, which we also discuss.

Willighagen E.L.,Uppsala University | Willighagen E.L.,Karolinska Institutet | Jeliazkova N.,Ideaconsult Ltd | Hardy B.,Douglas Connect | And 3 more authors.
BMC Research Notes | Year: 2011

Background: Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of predictive models. Such models rely strongly on being able to integrate information from many sources. The required integration of biological and chemical information sources requires, however, a common language to express our knowledge ontologically, and interoperating services to build reliable predictive toxicology applications. Findings. This article describes progress in extending the integrative bio- and cheminformatics platform Bioclipse to interoperate with OpenTox, a semantic web framework which supports open data exchange and toxicology model building. The Bioclipse workbench environment enables functionality from OpenTox web services and easy access to OpenTox resources for evaluating toxicity properties of query molecules. Relevant cases and interfaces based on ten neurotoxins are described to demonstrate the capabilities provided to the user. The integration takes advantage of semantic web technologies, thereby providing an open and simplifying communication standard. Additionally, the use of ontologies ensures proper interoperation and reliable integration of toxicity information from both experimental and computational sources. Conclusions: A novel computational toxicity assessment platform was generated from integration of two open science platforms related to toxicology: Bioclipse, that combines a rich scriptable and graphical workbench environment for integration of diverse sets of information sources, and OpenTox, a platform for interoperable toxicology data and computational services. The combination provides improved reliability and operability for handling large data sets by the use of the Open Standards from the OpenTox Application Programming Interface. This enables simultaneous access to a variety of distributed predictive toxicology databases, and algorithm and model resources, taking advantage of the Bioclipse workbench handling the technical layers. © 2011 Willighagen et al; licensee BioMed Central Ltd.

Sahlin U.,Lund University | Sahlin U.,Linnaeus University | Jeliazkova N.,Ideaconsult Ltd | Oberg T.,Linnaeus University
Molecular Informatics | Year: 2014

Predictive models used in decision making, such as QSARs in chemical regulation or drug discovery, call for evaluated approaches to quantitatively assess associated uncertainty in predictions. Uncertainty in less reliable predictions may be captured by locally varying predictive errors. In the current study, model-based bootstrapping was combined with analogy reasoning to generate predictive distributions varying in magnitude over a model's domain of applicability. A resampling experiment based on PLS regressions on four QSAR data sets demonstrated that predictive errors assessed by k nearest neighbour or weighted PRedicted Error Sum of Squares (PRESS) on samples of external test data or by internal cross-validation improved the performance of the uncertainty assessment. Analogy using similarity defined by Euclidean distances, or differences in standard deviation in perturbed predictions, resulted in better performances than similarity defined by distance to, or density of, the training data. Locally assessed predictive distributions had on average at least as good coverage as Gaussian distribution with variance assessed from the PRESS. An R-code is provided that evaluates performances of the suggested algorithms to assess predictive error based on log likelihood scores and empirical coverage graphs, and which applies these to derive confidence intervals or samples from the predictive distributions of query compounds. © 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Kochev N.T.,Plovdiv University | Paskaleva V.H.,Plovdiv University | Jeliazkova N.,Ideaconsult Ltd.
Molecular Informatics | Year: 2013

We present a new open source tool for automatic generation of all tautomeric forms of a given organic compound. Ambit-Tautomer is a part of the open source software package Ambit2. It implements three tautomer generation algorithms: combinatorial method, improved combinatorial method and incremental depth-first search algorithm. All algorithms utilize a set of fully customizable rules for tautomeric transformations. The predefined knowledge base covers 1-3, 1-5 and 1-7 proton tautomeric shifts. Some typical supported tautomerism rules are keto-enol, imin-amin, nitroso-oxime, azo-hydrazone, thioketo-thioenol, thionitroso-thiooxime, amidine-imidine, diazoamino-diazoamino, thioamide-iminothiol and nitrosamine-diazohydroxide. Ambit-Tautomer uses a simple energy based system for tautomer ranking implemented by a set of empirically derived rules. A fine-grained output control is achieved by a set of post-generation filters. We performed an exhaustive comparison of the Ambit-Tautomer Incremental algorithm against several other software packages which offer tautomer generation: ChemAxon Marvin, Molecular Networks MN.TAUTOMER, ACDLabs, CACTVS and the CDK implementation of the algorithm, based on the mobile H atoms listed in the InChI. According to the presented test results, Ambit-Tautomer's performance is either comparable to or better than the competing algorithms. Ambit-Tautomer module is available for download as a Java library, a command line application, a demo web page or OpenTox API compatible Web service. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Jeliazkova N.,Ideaconsult Ltd | Kochev N.,Plovdiv University
Molecular Informatics | Year: 2011

We present new developments in the AMBIT open source software package for efficient searching of chemical structures and structural fragments. AMBIT-SMARTS is a Java based software built on top of The Chemistry Development Kit. The AMBIT-SMARTS parser implements the entire SMARTS language specification with several syntax extensions that enable support for custom modifications introduced by third party software packages such as OpenEye, MOE and OpenBabel. The goal of yet another open-source SMARTS parser implementation is to achieve better performance and compatibility with multiple existing flavours of the SMARTS language, as well as to provide utilities for running efficient SMARTS queries in large structural databases. We describe a combination of approaches towards lowering the computational cost and improving the response time of substructure queries. An exhaustive comparison of the AMBIT algorithm with several subgraph isomorphism implementations is performed. To demonstrate the performance of the entire system from an end-user point of view, response time statistics for Web service substructure search queries against a database of 4.5M structures are also reported. The package has wide applicability in the implementation of various chemoinformatics tasks. It has already been used in several projects dealing with descriptor calculation and predictive algorithms, database queries, web applications and web services. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Jeliazkova N.,Ideaconsult Ltd. | Jeliazkov V.,Ideaconsult Ltd.
Journal of Cheminformatics | Year: 2011

The AMBIT web services package is one of the several existing independent implementations of the OpenTox Application Programming Interface and is built according to the principles of the Representational State Transfer (REST) architecture. The Open Source Predictive Toxicology Framework, developed by the partners in the EC FP7 OpenTox project, aims at providing a unified access to toxicity data and predictive models, as well as validation procedures. This is achieved by i) an information model, based on a common OWL-DL ontology ii) links to related ontologies; iii) data and algorithms, available through a standardized REST web services interface, where every compound, data set or predictive method has a unique web address, used to retrieve its Resource Description Framework (RDF) representation, or initiate the associated calculations. The AMBIT web services package has been developed as an extension of AMBIT modules, adding the ability to create (Quantitative) Structure-Activity Relationship (QSAR) models and providing an OpenTox API compliant interface. The representation of data and processing resources in W3C Resource Description Framework facilitates integrating the resources as Linked Data. By uploading datasets with chemical structures and arbitrary set of properties, they become automatically available online in several formats. The services provide unified interfaces to several descriptor calculation, machine learning and similarity searching algorithms, as well as to applicability domain and toxicity prediction models. All Toxtree modules for predicting the toxicological hazard of chemical compounds are also integrated within this package. The complexity and diversity of the processing is reduced to the simple paradigm "read data from a web address, perform processing, write to a web address". The online service allows to easily run predictions, without installing any software, as well to share online datasets and models. The downloadable web application allows researchers to setup an arbitrary number of service instances for specific purposes and at suitable locations. These services could be used as a distributed framework for processing of resource-intensive tasks and data sharing or in a fully independent way, according to the specific needs. The advantage of exposing the functionality via the OpenTox API is seamless interoperability, not only within a single web application, but also in a network of distributed services. Last, but not least, the services provide a basis for building web mashups, end user applications with friendly GUIs, as well as embedding the functionalities in existing workflow systems. © 2011 Jeliazkova and Jeliazkov; licensee Chemistry Central Ltd.

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