Storing the wisdom: chemical concepts and chemoinformatics

The purpose of the paper is to examine the nature of chemical concepts, and the ways in which they are applied in chemoinformatics systems. An account of concepts in philosophy and in the information sciences leads to an analysis of chemical concepts, and their representation. The way in which concepts are applied in systems for information retrieval and for structure–property correlation are reviewed, and some issues noted. Attention is focused on the basic concepts or substance, reaction and property, on the organising concepts of chemical structure, structural similarity, periodicity, and on more specific concepts, including two- and three-dimensional structural patterns, reaction types, and property concepts. It is concluded that chemical concepts, despite (or perhaps because of) their vague and mutable nature, have considerable and continuing value in chemoinformatics, and that an increased formal treatment of concepts may have value in the future

Storing the wisdom: chemical concepts and chemoinformatics

The purpose of the paper is to examine the nature of chemical concepts, and the ways in which they are applied in chemoinformatics systems. An account of concepts in philosophy and in the information sciences leads to an analysis of chemical concepts, and their representation. The way in which concepts are applied in systems for information retrieval and for structure–property correlation are reviewed, and some issues noted. Attention is focused on the basic concepts or substance, reaction and property, on the organising concepts of chemical structure, structural similarity, periodicity, and on more specific concepts, including two- and three-dimensional structural patterns, reaction types, and property concepts. It is concluded that chemical concepts, despite (or perhaps because of) their vague and mutable nature, have considerable and continuing value in chemoinformatics, and that an increased formal treatment of concepts may have value in the future

Development and application of a data-driven reaction classification model : comparison of an electronic lab notebook and the medicinal chemistry literature

Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables data-driven approaches to be developed. We present the development and validation of a 336-class machine learning-based classification model integrated within a Conformal Prediction (CP) framework in order to associate reaction class predictions with confidence estimations. We also propose a data-driven approach for 'dynamic' reaction fingerprinting to maximise the effectiveness of reaction encoding, as well as developing a novel reaction classification system that organises labels in four hierarchical levels (SHREC: Sheffield Hierarchical REaction Classification). We show that the performance of the CP augmented model can be improved by defining confidence thresholds to detect predictions that are less likely to be false. For example, the external validation of the model reports 95% of predictions as correct by filtering out less than 15% of the uncertain classifications. The application of the model is demonstrated by classifying two reaction datasets: one extracted from an industrial ELN and the other from the medicinal chemistry literature. We show how confidence estimations and class compositions across different levels of information can be used to gain immediate insights on the nature of reaction collections and hidden relationship between reaction classes

Enhancing Reaction-based de novo Design using Machine Learning

De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility. Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable. The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator. A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections. Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources

Extraction de connaissances à partir de bases de données de réactions en chimie organique

Colloque avec actes et comité de lecture. nationale.National audienceDans cet article, nous présentons un aspect de l'extraction de connaissances dans des bases de données de réactions chimiques. Ces bases de données sont de première importance, mais leur exploitation actuelle reste limitée à des interrogations classiques. Nous avons fait l'hypothèse que l'application de techniques de fouille de données à de telles bases peut faire émerger des éléments de connaissance sur les réactions, qui peuvent alors être réutilisés pour résoudre des problèmes de synthèse chimique. Pour mener à bien fouille de données et résolution de problème, la représentation et l'exploitation de connaissances du domaine est un préalable obligé. Les premiers résultats d'une expérience de fouille de données dans des bases de réactions sont présentés et analysés ici

Structure generation and de novo design using reaction networks

This project is concerned with de novo molecular design whereby novel molecules are built in silico and evaluated against properties relevant to biological activity, such as physicochemical properties and structural similarity to active compounds. The aim is to encourage cost-effective compound design by reducing the number of molecules requiring synthesis and analysis. One of the main issues in de novo design is ensuring that the molecules generated are synthesisable. In this project, a method is developed that enables virtual synthesis using rules derived from reaction sequences. Individual reactions taken from reaction databases were connected to form reaction networks. Reaction sequences were then extracted by tracing paths through the network and used to create ‘reaction sequence vectors’ (RSVs) which encode the differences between the start and end points of th esequences. RSVs can be applied to molecules to generate virtual products which are based on literature precedents. The RSVs were applied to structure-activity relationship (SAR) exploration using examples taken from the literature. They were shown to be effective in expanding the chemical space that is accessible from the given starting materials. Furthermore, each virtual product is associated with a potential synthetic route. They were then applied in de novo design scenarios with the aim of generating molecules that are predicted to be active using SAR models. Using a collection of RSVs with a set of small molecules as starting materials for de novo design proved that the method was capable of producing many useful, synthesisable compounds worthy of future study. The RSV method was then compared with a previously published method that is based on individual reactions (reaction vectors or RVs). The RSV approach was shown to be considerably faster than de novo design using RVs, however, the diversity of products was more limited

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Autonomous discovery in the chemical sciences part II: Outlook

This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to ``discover'' despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this article defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress toward the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.Comment: Revised version available at 10.1002/anie.20190998

The Value of New Scientific Communication Models for Chemistry

This paper is intended as a starting point for discussion on the possible future of scientific communication in chemistry, the value of new models of scientific communication enabled by web based technologies, and the necessary future steps to achieve the benefits of those new models. It is informed by a NSF sponsored workshop that was held on October 23-24, 2008 in Washington D.C. It provides an overview on the chemical communication system in chemistry and describes efforts to enhance scientific communication by introducing new web-based models of scientific communication. It observes that such innovations are still embryonic and have not yet found broad adoption and acceptance by the chemical community. The paper proceeds to analyze the reasons for this by identifying specific characteristics of the chemistry domain that relate to its research practices and socio-economic organization. It hypothesizes how these may influence communication practices, and produce resistance to changes of the current system similar to those that have been successfully deployed in other sciences and which have been proposed by pioneers within chemistry.National Science Foundation, Microsof