Royal Society of Chemistry open source cheminformatics platforms and libraries

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The cheminformatics group at the Royal Society of Chemistry has supported a number of national and international grants for the last 5 years with the deployment of Cheminformatics tools and platforms. Among them is the Open PHACTS project which is intended to help reducing the barriers to drug discovery in industry, academia and for small businesses by providing the Open PHACTS Discovery Platform. The PharmaSea project targets the identification of leads for new antibiotics in deep sea trenches. The National Chemistry Database Service and Data Repository is a collaborative platform for handling research data. Our contribution to all of these projects has been the development of a number of open source cheminformatics library and user interface components that can be freely used in 3rd party websites and applications. In this presentation we will cover our approach to the development as well as give particular examples of applications built using our toolkits. These software components will likely be very important in the future deployment of Open Notebook Science platforms to help deliver on the vision of Jean-Claude Bradley.

Software

RSC Open Source Cheminformatics
Platforms and Libraries
Valery Tkachenko
A Memorial Symposium celebrating
the work of Jean-Claude Bradley
Cambridge, UK
July 14th
2014

Chemical data entry
Unification attempt
Further simplification - pure technical view
Proof of concept designs
Proof of concepts applications
Where further?

Micropublishing article
Compounds
Reaction
Analytical Data
Text and References

Chemistry Validation and Standardization
Platform

• 3-year Innovative Medicines Initiative project
• Integrating chemistry and biology data using
semantic web technologies
• Open source code, open data and open
standards
• Academics, Pharmas, Publishers…
• To put medicines in the pipeline…

Chemical data entry
Simplification attempt
Further simplification - pure technical view
Proof of concept designs
Proof of concepts applications
Where further?

Handling complex content
What’s the
structure?
What’s the
structure?
Are they in
our file?
Are they in
our file?
What’s
similar?
What’s
similar?
What’s the
target?
What’s the
target?Pharmacology
data?
Pharmacology
data?
Known
Pathways?
Known
Pathways?
Working On
Now?
Working On
Now?Connections
to disease?
Connections
to disease?
Expressed in
right cell type?
Expressed in
right cell type?
Competitors?Competitors?
IP?IP?

Thank you
Email: tkachenkov@rsc.org
Slides: http://www.slideshare.net/valerytkachenko16

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The Open PHACTS project (openphacts.org) is a European initiative, constituting a public–private partnership to enable easier, cheaper and faster drug discovery. The project is supported by the OpenPHACTS Foundation (www.openphactsfoundation.org) and funded by contributions from several pharmaceutical companies. As part of Open PHACTS, a 'Chemical Registration Service” was created to register chemicals of interest to the project, allowing compound linkage between data sets. A key concept is the support for 'scientific lenses,' which allows hierarchical mapping of chemical entities, including supporting characteristics such as charge state, tautomerism and stereochemistry. Open PHACTS aggregated various databases, including ChEMBL, ChEBI, HMDB, DrugBank, PDB, MeSH, and WikiPathways. A new project builds on the Chemical Registration Service to establish an open chemistry registry and mapping service for general data set linkage. This expansion requires the support of multiple cheminformatics formats, the conversion and mapping of various identifiers, harmonized but configurable standardization, validation of the chemical structures, and the creation of new identifiers, to produce scientific lenses, or 'link sets'. Furthermore, these identifiers will be related to the compounds chemical names (IUPAC and trivial) and related chemical structures. This presentation will describe our ongoing work to create a fully open source, easy to install platform, which supports the ideas introduced by the Open PHACTS project and expands it with community data including, for example, the data now available from the EPA CompTox Chemistry Dashboard (comptox.epa.gov). This new platform supports chemical formats and provides for identifier conversion and cross-validation between datasets. The project is completely based on open source cheminformatics toolkits and available as a set of libraries, docker images and a web frontend based on FAIR and Open Data principles. The openness of this platform will allow for scientists to process their own datasets, and make them interoperable with other online chemical databases.

Open chemistry registry and mapping platform based on open source cheminforma...

Valery Tkachenko

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e-Research & the art of linking Astrophysics to Deforestation

David Wallom

Presentation delivered by Colin Batchelor from the RSC eScience team at ACS New Orleans Spring Meeting April 2013. There are dozens of public compound databases now available online, some of these providing access to tens of millions of chemical compounds. However, very little effort has been put into the delivery of databases of chemical reactions with the majority of large resources being commercial in nature. In our five years of delivering chemical based data resources to the chemistry community one of the primary requests has been that chemists want to know how to synthesize many of the chemicals they are researching. This presentation will provide an overview of our concerted efforts to enhance access to freely available chemistry data and will discuss the ChemSpider Reactions as an integrating hub of content including data extracted from US patents, from RSC Journals and databases and from our micro-publishing platform ChemSpider Synthetic Pages (CSSP).

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Ken Karapetyan

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ManjulaPatel

October 24, 2014: Joseph DeCarolis, Assistant Professor at North Carolina State University, will present The Importance of Open Data and Models for Energy Systems Analysis. Energy system models represent a critical planning tool that can be used to deliver policy-relevant insights at scales ranging from local to global. When such models are used to inform public policy, the associated data and source code should be open in order to enable third party replication of results, expose hidden assumptions, and identify key model sensitivities. In this talk, I describe my own effort to push open data and models within the international energy modeling community.

The Importance of Open Data and Models for Energy Systems Analysis

The Open Data Institute of North Carolina

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Mehr von Valery Tkachenko

Over the last few years we have seen a tremendous growth in various chemical databases. As a result we have now a variety of scientific resources, combined into a broad network and indexed through the directories like BioSharing and re3data. Such network, while growing quickly, is still in early days of adopting semantic web standards and does not yet support deep data indexing and discoverability, leave alone that mechanisms of intellectual properties protection are as simple as making data public or private at best. The lack of standards and well defined models to describe a scientific information structure even further inhibits free information flow which is essential for scientific discovery. In this talk we will share our experience spanning through decades of building chemical databases like PubChem, ChemSpider, OpenPHACTS and National Database Services and will outline fundamental problems associated with chemical databases as such as well as data quality and approaches for the modern architecture of the large-scale chemical databases.

Evolution of public chemistry databases: past and the future

Valery Tkachenko

Materials design is a grand challenge of materials science. And the main approach for solving this problem is still intuition-based. Such a way requires a lot of time and financial resources and months to years of conducting the experiment and doing characterization. Therefore, any kind of model that can be used at the very first stage of materials design and can narrow the selection area is a helpful tool for synthetic chemist. Also, an automated search for materials with human-defined target properties in the entire chemical space, i.e. inverse materials design is a highly desired tool in the exploration of materials design space. Along with that, de novo design is not a kind of a completely new task in a field of development of new organic molecules with target properties. A lot of different generative approaches are being used along with screening the libraries of existing molecules, searching for drugs for a particular target, or generating new ones based on a very simple initial structure. Here we would like to present a new approach for generating new materials with desired properties. We used autoencoder neural network architecture to encode materials composition and crystal structure as a vector in a latent space. In such case, any Quantitative Structure-Property Relationship (QSPR) model based on the vector can be interpreted as function in the latent space and can be used to predict property of existing materials as well as for prophetic ones. Such an approach has comparable accuracy with such classic computational methods as DFT in the case of predicting values of energies or charges, but significantly transcends them in terms of computational time. The proposed method was tested for generating super-firm materials, but can easily be extended to any target properties, granted a database of materials properties can be provided for training.

In silico design of new functional materials

Valery Tkachenko

Metal-organic frameworks (MOFs) attract a lot of interest due to their unique structure-dependent properties. Their internal pores comparable to the size of small molecules are naturally refined for various absorbance effects. Possessed properties lie in a foundation of multiple applications, such as catalysis, gas storage/separation and especially – clean energy related ones. Theoretical calculations are a usual way of decreasing experimental costs while investigating properties of new materials, especially at a design stage. Electronic structure calculations like density functional theory (DFT) in most cases provide an appropriate accuracy in matching experimentally measured data such as adsorbate interaction energies. However, as in the case of experimental studies, large-scale materials screening studies with DFT calculations are rather time-consuming, and it can be carried out only for structures with relatively small unit cell. Here we would like to present a theoretical and experimental results describing calculation of electron density in metal-organic frameworks. We built a model trained to predict partial charges on MOF atoms based on DFT calculations. The relative error of the model allows us to conclude that models do not decrease the level of accuracy and do not superinduce additional error comparing to DFT. At the same time, computational cost of the model is several orders of magnitude less. Models also demonstrated transferability and allowed to make prediction e.g. for MOFs containing metals not presented in the train set. We have also built a force-field (FF) of two-centered and three-centered interatomic potentials constructed using predicted charges. The FF proved to reproduce MOF crystal structure. As a final test, we have applied the developed model and FF to a new synthesized lanthanide-containing MOFs to estimate influence of supramolecular effects on metal complexation selectivity. As a result, we’ve built a model predicting one of basic MOF properties within relatively small computational time and tested it on experimental data, both obtained from literature sources and self-investigated.

Metal-organic frameworks: from database to supramolecular effects in complexa...

Valery Tkachenko

Public repositories containing diverse chemical and biological data are one of the main sources of knowledge for further biomedical research. Unfortunately, extraction and transforming these data into a well-interpretable form is a complex exercise. Ongoing efforts of a community are mainly focused on the analysis of co-occurrences of terms, text annotation based on terms similarity and related tasks [1]. Here we present an approach based on natural-language processing techniques, which is intended to shift the focus of a search for similar texts on chemical topics from word- to document-level. PubMed records were used to implement word2vec and doc2vec models. Generated text representations can be used to search for similar abstracts; the similarity is more dependent on this representation than the co-presence of certain terms (neighbor compounds, similar publication date, etc.). Document-level clustering was also implemented to provide insight into the PubMed text corpus structure. This approach can serve as an alternative to standard topic modeling techniques for the discovery of hidden semantic features in an unsupervised manner.

Abstract recommendation system: beyond word-level representations

Valery Tkachenko

In the last decade there is an increasing interest in using in silico tools for potential risk assessment of newly released chemicals due to the large number of chemicals enter the market yearly and the big uncertainty on their possible hazardous effects. Different tools and methods based on machine learning techniques already exist and were used in a wide range of applications starting from quantitative structure-property relationships and expanding into predictive toxicology. There is a lot of historical data accumulated across multiple databases which is publicly available and can be used with novel machine learning methods. Unfortunately, due to different datasets, metrics and validation strategies, the significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. This work is an attempt to develop a multitask system which can serve as searchable curated collections of multiple chemical datasets and ready to use novel machine learning methods solely built using open source frameworks and libraries. We have implemented a set of self-tuned, using grid search and k-fold validation, traditional machine learning methods (shallow methods) such as Naïve Bayes, k-Nearest Neighbors, Random Forest, Boosted Decision Trees, Regularized Logistic Regression, and Support Vector Machines base on open source Scikitlearn (http://scikit-learn.org/stable/). The novel Deep Neural Networks models of different complexity have been also implemented using Keras (https://keras.io/), a deep learning open library, and a Tensorflow (www.tensorflow.org) as a backend. The machine learning models were trained and evaluated to predict measures of toxicity from the physical characteristics of the structure of chemicals using the same datasets as in the Toxicity Estimation Software Tool (https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test). The Deep Learning models showed very good performance evaluation characteristics and were found to be useful in predicting of toxicological and physicochemical parameter endpoints. The results of this work support an optimistic view that some of current obstacles in cheminformatics can be overcome by using Deep Learning methods.

Machine learning methods for chemical properties and toxicity based endpoints

Valery Tkachenko

Acquisition of data from public sources is inefficient, time consuming and limited in scope. The NIH has recently posted its intention to financially support data deposition by investigators through the ‘data sharing plan' for each funded proposal. However, this plan also points to a current weakness of the centralized data sharing and acquisition as all laboratories use different data collection and formatting approaches. These inconsistencies in data formatting by individual labs leads to the need to invest significant resources in data curation and interpretation by the technical staff involved in the maintenance of the centralized data collection resource such as CaNanoLab or Nanomaterial Registry. It would be far more efficient and useful if there were a standardized data collection and deposition template with standard key terms (such as Minimal Information about Nanomaterials, MIAN) that could be modified to add new or important additional data or parameters for each investigator. These new features cold be ultimately adopted in the classification scheme and guide the scope of the expanding database. This approach would be a win-win as it would enable structure for the investigators laboratory, consistency in data reporting and a means of transmitting data to the database in parallel to publication to eliminate the acquisition step from the process. In this talk we will outline our experience building Open Science Data Repository, a federated database system for direct acquisition, curation and management of research data, including nanomaterial data capture, transformation, and streamlined submission to nanomaterial knowledgebases. The key part of the system is microservices based architecture which exposes RESTful API suitable for direct integration into Workflow Management Systems as well as built-in modules facilitating and enforcing various lab-specific standard operating procedures.

Chemical workflows supporting automated research data collection

Valery Tkachenko

Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.

Deep learning methods applied to physicochemical and toxicological endpoints

Valery Tkachenko

While we have seen a tremendous growth in machine learning methods over the last two decades there is still no one fits all solution. The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as Deep Learning (DL). There has been increasing use of DL recently which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts. It was therefore our goal to develop a DL framework embedded into a general research data management platform (Open Science Data Repository) which can be used as an API, standalone tool or integrated in new software as an autonomous module. In this poster we will present results of comparing performance of classic machine learning methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) with Deep Learning and will discuss challenges associated with Ddeep Learning Neural Networks (DNN). The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/) and Tensorflow (www.tensorflow.org) and applied to various use cases connected to prediction of physicochemical properties, ADME, toxicity and calculating properties of materials. It was also shown that using nVidia GPUs significantly accelerates calculations, although memory consumption puts some limits on performance and applicability of standard toolkits 'as is'.

Deep Learning on nVidia GPUs for QSAR, QSPR and QNAR predictions

Valery Tkachenko

There is a variety of public resources on the Internet which contain information about various aspects of chemical, biological and pharmaceutical domains. The quality, maturity, hosting organizations, team sizes behind these data resources vary wildly and as a consequence content cannot be always trusted and the effort of extracting information and preparing it for reuse is repeated again and again at various levels. This problem is especially serious in applications for QSAR, QSPR and QNAR modeling. On the other hand authors of this poster believe, based on their own extensive experience building various types of chemical, analytical and biological databases for decades, that the process of building such knowledgebase can be systematically described and automated. This poster will outline the work performed on text and data-mining various public resources on the Web, data curation process and making this information publicly available through a portal and a RESTful API. We will also demonstrate how such knowledgebase can be used for real-time QSAR and QSPR predictions.

Using publicly available resources to build a comprehensive knowledgebase of ...

Valery Tkachenko

There are a large number of US government databases housing diverse collections of chemical data including bioassay data (PubChem), toxicity data (CompTox Chemistry Dashboard) and environmental data (a large collection of EPA databases), to name just a few. In many cases integration between the databases, at the chemical structure level, is via alphanumeric text identifiers such as CAS Numbers, or via InChI (International Chemical Identifiers). Structure-based integration is hyper-dependent on the initial inputs providing the chemical structures to the InChI generation algorithm. To ensure optimal integration between various databases, community standards and agreement regarding standardization of chemical structures would be beneficial, not only to integration of US government databases and resources but also to the international scientific community and hosts of online databases. This presentation will discuss our progress to deliver a fully Open Source chemical standardization platform as an exemplar for the community to build on and enhance. The system utilizes the CDK (Chemistry Development Kit), RD Kit and other open source components. The resource expands on our previous work regarding the Chemical Validation and Standardization Platform and has been tested using the open data collection provided by the EPA Comptox Chemistry Dashboard.

Need and benefits for structure standardization to facilitate integration and...

Valery Tkachenko

The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as deep learning. There has been increasing use of deep learning which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts, it is currently not in any of the major cheminformatics tools. It is therefore our goal to develop a deep learning algorithm and toolkit which can be used as a standalone or integrated in new software being developed by us such as the Open Science Data Repository (OSDR). We will show how classic machine learning (CML) methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) compares to cutting edge deep learning and talk about challenges associated with deep neural networks (DNN) learning models. The open source Scikit-learn (http://scikit-learn.org/stable/) ML python library was used for building, tuning, and validating all CML models. The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/), a deep learning library, and Tensorflow (www.tensorflow.org) as a backend. All the developed pipelines consist of stratified splitting of the input dataset into train (80%) and test (20%) datasets. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were computed for each model for ADME/Tox and other physicochemical properties. DNN learning models were found to be very good in predicting activities and can outperform most of the CML models.

Development and comparison of deep learning toolkit with other machine learni...

Valery Tkachenko

Over years a multitude of chemical formats and approaches were created to address various aspects of handling chemical information and building databases of chemical knowledge. As a result the current state of this landscape is severely affected by the lack of well-accepted and community-recognized formats, protocols, metadata standards, validation routines and standards in handling, storing and representation, lack of open toolkits which conform to the same standards as well as the lack of platforms which allow interactive and collaborative work to solve all the above problems. While such organizations as RDA and IUPAC as well as some government agencies and institutes are concerned and trying to address the problem it is still a severe pain point. In this presentation we will talk about our experience of building a federated knowledgebase called Open Science Data Repository which supports deposition of raw and structured chemical and analytical data in various formats, runs validation and standardization protocols, is build in a highly modular way that allows using both its API and its components in a Cloud or to be deployed on premises behind firewalls and supports a variety of use cases including collaborative data curation, rich analytics and visualization, real-time machine learning, formats conversion and preparing depositions into PubChem and ChemSpider from a variety of sources and fully supports FAIR principles for research data.

Living in a world of federated knowledge challenges, principles, tools and ...

Valery Tkachenko

Structured Product Labeling (SPL) is a document markup standard approved by the Health Level Seven (HL7) standards organization and adopted by the FDA as a mechanism for exchanging product and facility information. Product information provided by companies in SPL format may be accessed from the FDA Online Label Repository (labels.fda.gov) and the National Library of Medicine DailyMed web site (dailymed.nlm.nih.gov). FDA also maintains and publishes SPL Indexing Files for Pharmacologic Class, Substance, Product Concept, Biological Drug Substance, and Billing Units. Data from the Indexing Files can be linked to data in both SPL resources and external resources via chemical and non-chemical identifiers. In this talk we will present on the latest addition to SPL which allows indexing data on proteins, polymers and structurally diverse substances. We will also discuss the potential value of SPL to the integration between public chemistry databases, especially those hosted by the United States Government.

Using the structured product labeling format to index versatile chemical data

Valery Tkachenko

Sustainable research progress in many scientific disciplines critically depends on the existence of robust specialized databases that integrate and structure all available experimental information in the respective fields. The need for such reference database is especially critical for nanoscience and nanomaterial research given the significant diversity of shapes, sizes, and properties of engineered nanomaterials and the difficulty of synthesizing engineered nanoparticles with controlled properties. The acquisition of data from public sources is inefficient, time consuming and limited in scope. Moreover, it is not clear where the resources come from to support this activity on a perpetual basis. The NIH has recently posted its intention to provide special funds toward data deposition by the experimental investigators through the ‘data sharing plan’ for each proposal. However, this points to a current weakness which is that all laboratories use different data collection approaches each of which requires interpretation by staff hosting the database. It would be far more efficient and useful if a template with key terms that could be modified to add new or important additional data or parameters for each investigator. We will discuss tools and approaches to facilitate collection and direct deposition of experimental data into Nanomaterial Registry (https://www.nanomaterialregistry.org/) - a versatile semantically enriched templates-based platform for registering diverse data pertaining to nanomaterials research.

Tools and approaches for data deposition into nanomaterial databases

Valery Tkachenko

Over the last few years we have seen a tremendous growth in various data repositories pushed and supported by funding bodies and various data preservation initiatives. As a result we have now a variety of scientific resources, combined into a broad network and indexed through the directories like BioSharing and re3data. Such network, while growing quickly, is still in early days of adopting semantic web standards and does not yet support deep data indexing and discoverability, leave alone that mechanisms of intellectual properties protection are as simple as making data public or private at best. The lack of standards and well defined models to describe a scientific information structure even further inhibits free information flow which is essential for scientific discovery. One of the most affected areas is not surprisingly materials sciences where due to the inherent complexity of the field of study the situation is even more severe. In this talk we present a chemistry information platform designed to support a variety of data formats along with metadata, sophisticated ways of collaboration and secure data exchanges. We will discuss challenges that we have faced developing such platform as well as solutions that we have came with.

Open Science Data Repository - the platform for materials research

Valery Tkachenko

OpenPHACTS - Chemistry Platform Update and Learnings

Valery Tkachenko

Evolution of open chemical information

Valery Tkachenko

In last few years the number and the size of chemical databases has been steadily increasing, as has the complexity of information residing in those databases creating truly multidimensional chemical spaces. Yet the most common user interface approach still remains based on search-and-browse workflow thus essentially preventing a proper navigation through such databases and hiding data patterns which may belong to other dimensions. As we at the Royal Society of Chemistry are building a chemical database service it is potentially useful to be able to visualize large chemical spaces, ranging in size from tens of thousands to tens of millions of compounds. Dimensionality reduction techniques such as PCA have been used to produce two-dimensional displays of large chemical spaces, via the production of scatterplots. Standard chart-plotting libraries allow interactive scatterplots to be produced, but do not scale well to large numbers of data points. Our new visualisation tool, OMPOL, is a browser-based tool for displaying and interacting with these data sets, allowing people to smoothly and responsively pan and zoom these plots, view the names and structures associated with the data points, select regions of chemical space and find typical and atypical members of those regions.

OMPOL – visualisation of large chemical spaces

Valery Tkachenko

The need for a high quality reaction database underpins synthetic reaction planning, as highlighted by the roadmap of the Dial-a-Molecule grand challenge [1] (the aims of which are to be able to predict the outcome of a reaction a priori and therefore generate products on demand, and also to optimise a reaction). A number of reaction databases are available [2] - most of these focus on storing basic reaction schemas and details and link to publications for more details. However their main limitation is that because their major source is the abstraction of published literature, insufficient structured reaction detail is recorded: for someone else to reproduce the reaction to fully record all reaction products (not just the target product) previous attempts to reach the optimised reaction route so that this "work-up" can be correlated to allow better prediction of reaction outcomes. As a result, the reactions domain of the chemical data repository that the Royal Society of Chemistry is developing will capture: reactions and processes directly from Electronic Lab Notebooks reactions which gave low yields or unintended products processes, parameters and equipment in S88 process recipe [3] style for maximum reproducibility multistep reactions reactants, products etc. not just as small organic molecules raw characterisation data linked to products We will demonstrate a first version, populated with reactions text-mined from RSC articles and examples of notebook reactions and processes as recorded by an academic research group at Cornell University. [1] Dial a Molecule Grand Challenge, http://generic.wordpress.soton.ac.uk/dial-a-molecule/ (accessed Oct 8, 2015) [2] Organic Chemistry Resources Worldwide, http://www.organicworldwide.net/content/reaction-databases (accessed Oct 8, 2015) [3] ISA, "Batch Control Part 1: Model and Terminology," The International Society for Measurement and Control, ISA Press, ISA - S88.01-1995

Not just another reaction database

Valery Tkachenko

The Royal Society of Chemistry hosts an increasing number of chemistry related databases and have utilized SQL-based technologies for our development platforms in general. In recent years the interest in noSQL databases has exploded as the associated technologies have developed and have shown great promise in terms of enhanced performance. We have collaborated with GGA Software Services to implement their noSQL technologies and have integrated it into the compound repository presently being developed as part of the underpinning architecture for compound data management at the RSC. This presentation will provide an overview of the reasons why we have integrated a noSQL solution, quantitative analysis of the benefits of inclusion and our thoughts regarding further approaches to optimize search performance for the chemical compound repository.

Experiences and adventures with no sql and its applications to cheminformatic...

Valery Tkachenko

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