Semantic Similarity in Cheminformatics

Similarity in chemistry has been applied to a variety of problems: to predict biochemical properties of molecules, to disambiguate chemical compound references in natural language, to understand the evolution of metabolic pathways, to predict drug-drug interactions, to predict therapeutic substitution of antibiotics, to estimate whether a compound is harmful, etc. While measures of similarity have been created that make use of the structural properties of the molecules, some ontologies (the Chemical Entities of Biological Interest (ChEBI) being one of the most relevant) capture chemistry knowledge in machine-readable formats and can be used to improve our notions of molecular similarity. Ontologies in the biomedical domain have been extensively used to compare entities of biological interest, a technique known as ontology-based semantic similarity. This has been applied to various biologically relevant entities, such as genes, proteins, diseases, and anatomical structures, as well as in the chemical domain. This chapter introduces the fundamental concepts of ontology-based semantic similarity, its application in cheminformatics, its relevance in previous studies, and future potential. It also discusses the existing challenges in this area, tracing a parallel with other domains, particularly genomics, where this technique has been used more often and for longer

Ontologies4Chem: The landscape of ontologies in chemistry

For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows

ChEBI in 2016: Improved services and an expanding collection of metabolites

ChEBI is a database and ontology containing infor-mation about chemical entities of biological inter-est. It currently includes over 46 000 entries, each of which is classified within the ontology and assigned multiple annotations including (where relevant) a chemical structure, database cross-references, syn-onyms and literature citations. All content is freely available and can be accessed online a

Ontologies in medicinal chemistry: current status and future challenges

[Abstract] Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.Instituto de Salud Carlos III; FIS-PI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/21

Recommender system to support comprehensive exploration of large scale scientific datasets

Bases de dados de entidades científicas, como compostos químicos, doenças e objetos astronómicos, têm crescido em tamanho e complexidade, chegando a milhares de milhões de itens por base de dados. Os investigadores precisam de ferramentas novas e inovadoras para auxiliar na escolha desses itens. Este trabalho propõe o uso de Sistemas de Recomendação para auxiliar os investigadores a encontrar itens de interesse. Identificamos como um dos maiores desafios para a aplicação de sistemas de recomendação em áreas científicas a falta de conjuntos de dados padronizados e de acesso aberto com informações sobre as preferências dos utilizadores. Para superar esse desafio, desenvolvemos uma metodologia denominada LIBRETTI - Recomendação Baseada em Literatura de Itens Científicos, cujo objetivo é a criação de conjuntos de dados , relacionados com campos científicos. Estes conjuntos de dados são criados com base no principal recurso de conhecimento que a Ciência possui: a literatura científica. A metodologia LIBRETTI permitiu o desenvolvimento de novos algoritmos de recomendação específicos para vários campos científicos. Além do LIBRETTI, as principais contribuições desta tese são conjuntos de dados de recomendação padronizados nas áreas de Astronomia, Química e Saúde (relacionado com a doença COVID-19), um sistema de recomendação semântica híbrido para compostos químicos em conjuntos de dados de grande escala, uma abordagem híbrida baseada no enriquecimento sequencial (SeEn) para recomendações sequenciais, um pipeline baseado em semântica de vários campos para recomendar entidades biomédicas relacionadas com a doença COVID-19.Databases for scientific entities, such as chemical compounds, diseases and astronomical objects, are growing in size and complexity, reaching billions of items per database. Researchers need new and innovative tools for assisting the choice of these items. This work proposes the use of Recommender Systems approaches for helping researchers to find items of interest. We identified as one of the major challenges for applying RS in scientific fields the lack of standard and open-access datasets with information about the preferences of the users. To overcome this challenge, we developed a methodology called LIBRETTI - LIterature Based RecommEndaTion of scienTific Items, whose goal is to create datasets related to scientific fields. These datasets are created based on scientific literature, the major resource of knowledge that Science has. LIBRETTI methodology allowed the development and testing of new recommender algorithms specific for each field. Besides LIBRETTI, the main contributions of this thesis are standard and sequence-aware recommendation datasets in the fields of Astronomy, Chemistry, and Health (related to COVID-19 disease), a hybrid semantic recommender system for chemical compounds in large-scale datasets, a hybrid approach based on sequential enrichment (SeEn) for sequence-aware recommendations, a multi-field semantic-based pipeline for recommending biomedical entities related to COVID-19 disease

Self-organizing ontology of biochemically relevant small molecules

<p>Abstract</p> <p>Background</p> <p>The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest.</p> <p>Results</p> <p>To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development.</p> <p>Conclusions</p> <p>We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development.</p

ART: An ontology based tool for the translation of papers into Semantic Web format

The paper describes initial work on an ontology based tool, ART, for the semantic annotation of papers stored in digital repositories. ART is intended for the annotation not only of data and metadata about a paper, but also the main elements of the described scientific investigation, such as goals, hypotheses, and observations. ART will also be able to aid in the expression of research results directly in a semantic format, through the composition of text using ontology-based templates and stored typical key phrases for the description of basic elements of the research. ART&apos;s system design, its functionality, and related projects are discussed. An example annotated paper is presented in order to demonstrate the expected output of the tool.

Automated tagging of environmental data using a novel SKOS formatted environmental thesaurus

There is increasing need to use the widest range of data to address issues of environmental management and change, which is reflected in increasing emphasis from government funding agencies for better management and access to environmental data. Bringing together different environmental datasets to confidently enable integrated analysis requires reference to common standards and definitions, which are frequently lacking in environmental data, due to the broad subject area and lack of metadata. Automatic inclusion within datasets of controlled vocabulary concepts from publicly available standard vocabularies facilitates accurate annotation and promotes efficiency of metadata creation. To this end, we have developed a thesaurus capable of describing environmental chemistry datasets. We demonstrate a novel method for tagging datasets, via insertion of this thesaurus into a Laboratory Information Management System, enabling automated tagging of data, thus promoting semantic interoperability between tagged data resources. Being web available, and formatted using the Simple Knowledge Organisation System (SKOS) semantic standard, this thesaurus is capable of providing links both to and from other relevant thesauri, thus facilitating a linked data approach. Future developments will see extension of the thesaurus by the user community, in terms of both concepts included and links to externally hosted vocabularies. By employing a Linked Open Data approach, we anticipate that Web-based tools will be able to use concepts from the thesaurus to discover and link data to other information sources, including use in national assessment of the extent and condition of environmental resources