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Clark A.M.,Molecular Materials Informatics Inc. | Ekins S.,Collaborations in Chemistry | Williams A.J.,Royal Society of Chemistry
Molecular Informatics | Year: 2012

The proliferation of mobile devices such as smartphones and tablet computers has recently been extended to include a growing ecosystem of increasingly sophisticated chemistry software packages, commonly known as apps. The capabilities that these apps can offer to the practicing chemist are approaching those of conventional desktop-based software, but apps tend to be focused on a relatively small range of tasks. To overcome this, chemistry apps must be able to seamlessly transfer data to other apps, and through the network to other devices, as well as to other platforms, such as desktops and servers, using documented file formats and protocols whenever possible. This article describes the development and state of the art with regard to chemistry-aware apps that make use of facile data interchange, and some of the scenarios in which these apps can be inserted into a chemical information workflow to increase productivity. A selection of contemporary apps is used to demonstrate their relevance to pharmaceutical research. Mobile apps represent a novel approach for delivery of cheminformatics tools to chemists and other scientists, and indications suggest that mobile devices represent a disruptive technology for drug discovery, as they have been to many other industries. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Ekins S.,Collaborations in Chemistry | Clark A.M.,Molecular Materials Informatics Inc. | Williams A.J.,Royal Society of Chemistry
Molecular Informatics | Year: 2012

The Open Drug Discovery Teams (ODDT) project provides a mobile app primarily intended as a research topic aggregator of predominantly open science data collected from various sources on the internet. It exists to facilitate interdisciplinary teamwork and to relieve the user from data overload, delivering access to information that is highly relevant and focused on their topic areas of interest. Research topics include areas of chemistry and adjacent molecule-oriented biomedical sciences, with an emphasis on those which are most amenable to open research at present. These include rare and neglected diseases, and precompetitive and public-good initiatives such as green chemistry. The ODDT project uses a free mobile app as user entry point. The app has a magazine-like interface, and server-side infrastructure for hosting chemistry-related data as well as value added services. The project is open to participation from anyone and provides the ability for users to make annotations and assertions, thereby contributing to the collective value of the data to the engaged community. Much of the content is derived from public sources, but the platform is also amenable to commercial data input. The technology could also be readily used in-house by organizations as a research aggregator that could integrate internal and external science and discussion. The infrastructure for the app is currently based upon the Twitter API as a useful proof of concept for a real time source of publicly generated content. This could be extended further by accessing other APIs providing news and data feeds of relevance to a particular area of interest. As the project evolves, social networking features will be developed for organizing participants into teams, with various forms of communication and content management possible. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Clark A.M.,Molecular Materials Informatics Inc. | Ekins S.,Collaborations Pharmaceuticals Inc. | Ekins S.,Collaborative Drug Discovery, Inc.
Journal of Chemical Information and Modeling | Year: 2015

In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens. (Figure Presented). © 2015 American Chemical Society.


Ekins S.,Collaborations in Chemistry | Clark A.M.,Molecular Materials Informatics Inc. | Williams A.J.,Royal Society of Chemistry
ACS Sustainable Chemistry and Engineering | Year: 2013

Green Chemistry related information is generally proprietary, and papers on the topic are commonly behind pay walls that limit their accessibility. Several new mobile applications (apps) have been recently released for the Apple iOS platform, which incorporate green chemistry concepts. Because of the large number of people who now own a mobile device across all demographics, this population represents a highly novel way to communicate green chemistry, which has not previously been appreciated. We have made the American Chemical Society Green Chemistry Institute (ACS GCI) Pharmaceutical Roundtable Solvent Selection Guide more accessible and have increased its visibility by creating a free mobile app for the Apple iOS platform called Green Solvents. We have also used this content for molecular similarity calculations using additional solvents to predict potential environmental and health categories, which could help in solvent selection. This approach predicted the correct waste or health class for over 60% of solvents when the Tanimoto similarity was >0.5. Additional mobile apps that incorporate green chemistry content or concepts are also described including Open Drug Discovery Teams and Yield101. Making green chemistry information freely available or at very low cost via such apps is a paradigm shift that could be exploited by content providers and scientists to expose their green chemistry ideas to a larger audience. © 2012 American Chemical Society.


Ekins S.,Collaborative Drug Discovery, Inc. | Pottorf R.,Rutgers University | Reynolds R.C.,University of Alabama at Birmingham | Williams A.J.,Royal Society of Chemistry | And 2 more authors.
Journal of Chemical Information and Modeling | Year: 2014

Selecting and translating in vitro leads for a disease into molecules with in vivo activity in an animal model of the disease is a challenge that takes considerable time and money. As an example, recent years have seen whole-cell phenotypic screens of millions of compounds yielding over 1500 inhibitors of Mycobacterium tuberculosis (Mtb). These must be prioritized for testing in the mouse in vivo assay for Mtb infection, a validated model utilized to select compounds for further testing. We demonstrate learning from in vivo active and inactive compounds using machine learning classification models (Bayesian, support vector machines, and recursive partitioning) consisting of 773 compounds. The Bayesian model predicted 8 out of 11 additional in vivo actives not included in the model as an external test set. Curation of 70 years of Mtb data can therefore provide statistically robust computational models to focus resources on in vivo active small molecule antituberculars. This highlights a cost-effective predictor for in vivo testing elsewhere in other diseases. © 2014 American Chemical Society.


Clark A.M.,Molecular Materials Informatics Inc. | Sarker M.,SRI International | Ekins S.,Collaborative Drug Discovery, Inc.
Journal of Cheminformatics | Year: 2014

We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. Results: We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP-6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. Conclusions: TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool. © 2014 Clark et al.


Ekins S.,Collaborative Drug Discovery, Inc. | Clark A.M.,Molecular Materials Informatics Inc. | Sarker M.,SRI International
Journal of Cheminformatics | Year: 2013

Background: An increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs. Results: In order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active. Conclusions: TB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts. © 2013 Ekins et al.; licensee Chemistry Central Ltd.


Clark A.M.,Molecular Materials Informatics Inc.
Molecular Informatics | Year: 2013

The creation of 2D molecular structure diagrams that make full use of the capabilities of modern display systems, using only input data expressed in file formats used for cheminformatics, is a complex task that requires a number of additional algorithms. Assuming that atom positions have been well chosen, the rendering engine is required to micromanage the precise positioning of atom labels, bonds and atom adjuncts, in such a way that the final output is correct, consistent with convention, and as pleasing to the eye as a diagram produced by a graphic designer. The techniques must be equally applicable when creating output for low-resolution screens and high resolution printed output, and make use of contemporary graphics file formats in such a way that the largest possible number of software platforms are able to display the output at any resolution without degradation or inconsistency. The main issues involved in meeting these criteria are discussed, and algorithms for satisfying them are presented. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Clark A.M.,Molecular Materials Informatics Inc.
Journal of Chemical Information and Modeling | Year: 2011

Most data structures used to represent molecular entities for cheminformatics are underspecified for purposes of representing nonorganic chemical species. Two extensions are proposed: allowing bond orders of 0 and adding an atom property to control the number of inferred attached hydrogen atoms. The case for these two extensions is made by demonstrating the effective representation of a number of unconventional bonding types that cannot be effectively represented by data structures currently in common use. A set of enhancements to the industry standard MDL CTfile format is proposed, which includes a backward compatibility mechanism to maximize interpretability by software that has not been updated to make use of the extensions. © 2011 American Chemical Society.


Clark A.M.,Molecular Materials Informatics Inc.
Journal of Cheminformatics | Year: 2010

A collection of primitive operations for molecular diagram sketching has been developed. These primitives compose a concise set of operations which can be used to construct publication-quality 2 D coordinates for molecular structures using a bare minimum of input bandwidth. The input requirements for each primitive consist of a small number of discrete choices, which means that these primitives can be used to form the basis of a user interface which does not require an accurate pointing device. This is particularly relevant to software designed for contemporary mobile platforms. The reduction of input bandwidth is accomplished by using algorithmic methods for anticipating probable geometries during the sketching process, and by intelligent use of template grafting. The algorithms and their uses are described in detail. © 2010 Clark; licensee BioMed Central Ltd.

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