Antony J. Williams

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Internet-based tools for communication and collaboration in chemistry

Web-based technologies, coupled with a drive for improved communication between scientists, have resulted in the proliferation of scientific opinion, data and knowledge at an ever-increasing rate. The availability of tools to host wikis and blogs has provided the necessary building blocks for scientists with only a rudimentary understanding of computer software science to communicate to the masses. This newfound freedom has the ability to speed up research and sharing of results, develop extensive collaborations, conduct science in public, and in near-real time. The technologies supporting chemistry, while immature, are fast developing to support chemical structures and reactions, analytical data support and integration to related data sources via supporting software technologies. Communication in chemistry is already witnessing a new revolution.

Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation

In recent years there has been a dramatic increase in the number of freely accessible online databases serving the chemistry community. The internet provides chemistry data that can be used for data-mining, for computer models, and integration into systems to aid drug discovery. There is however a responsibility to ensure that the data are high quality to ensure that time is not wasted in erroneous searches, that models are underpinned by accurate data and that improved discoverability of online resources is not marred by incorrect data. In this article we provide an overview of some of the experiences of the authors using online chemical compound databases, critique the approaches taken to assemble data and we suggest approaches to deliver definitive reference data sources.

A Predictive Ligand-Based Bayesian Model for Human Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and α-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.

Mobile apps for chemistry in the world of drug discovery

Mobile hardware and software technology continues to evolve very rapidly and presents drug discovery scientists with new platforms for accessing data and performing data analysis. Smartphones and tablet computers can now be used to perform many of the operations previously addressed by laptops or desktop computers. Although the smaller screen sizes and requirements for touch-screen manipulation can present user-interface design challenges, especially with chemistry-related applications, these limitations are driving innovative solutions. In this early review of the topic, we collectively present our diverse experiences as software developer, chemistry database expert and naïve user, in terms of what mobile platforms could provide to the drug discovery chemist in the way of applications in the future as this disruptive technology takes off.

A perspective of publicly accessible/open-access chemistry databases.

The Internet has spawned access to unprecedented levels of information. For chemists the increasing number of resources they can use to access chemistry-related information provides them a valuable path to discovery of information, one which was previously limited to commercial and therefore constrained resources. The diversity of information continues to expand at a dramatic rate and, coupled with an increasing awareness for quality, curation and improved tools for focused searches, chemists are now able to find valuable information within a few seconds using a few keystrokes. This shift to publicly available resources offers great promise to the benefits of science and society yet brings with it increasing concern from commercial entities. This article will discuss the benefits and disruptions associated with an increase in publicly available scientific resources.

Reaching Out to Collaborators: Crowdsourcing for Pharmaceutical Research

Doing more with less resources used to be a situation common just for academic scientists. This is unfortunately still true for academics, but we are seeing others facing many of the same challenges. With the squeeze on budgets and cost-cutting resulting from recent worldwide economic challenges, the failure of many drugs to make it through the pipeline to the market, and the increasing costs associated with the drug development process, we are now seeing in the pharmaceutical industry a dramatic shift, perhaps belatedly, to have to accommodate similar challenges of doing more with less. This situation could also represent the further crumbling of a 150year-old-plus paradigm of the large company being the predominant source for developing therapeutics for profit. We are also seeing increased discussion about different models of facilitating pharmaceutical research as well as the suggestion of opportunities to collaborate and use tools that perhaps would not have been considered in the past (1–4). This shift to “openness” in certain areas, specifically the sharing of precompetitive data and processes, parallels the societal shifts we have seen in so many areas of open-source software development, the sharing of data, and the utility of free data resources and repositories, such as PubChem (http://pubchem.ncbi.nlm. nih.gov/) and others (see Table I). From the extreme of keeping entire projects in house, there is a shift to decentralized research. One view of pharmaceutical research is to use loose networks of external researchers from companies, academics or consultants, create a community around a shared interest and gather their ideas. We think this comes closest to the ideal of crowdsourcing where the wisdom of the many and their varied perspectives can benefit community-based efforts whether in software, knowledge capture, etc. The loose definition of crowdsourcing as “outsourcing a task to a group or community of people in an open call” is a relatively new phenomenon, culture or movement, which is best summarized in the book “Wikinomics, How Mass Collaboration Changes Everything” (5). Living in our connected world, pharmaceutical researchers can communicate in a variety of ways (4) to leverage ideas from around the globe. These ideas do not have to come from within the walls of a single organisation. Taking this further: why limit access to just ideas? Open tools and data could feed an ecosystem. They could also breed a new class of researcher without affiliation, who has allegiance to neither company nor research organization. They test their hypotheses with data from elsewhere, they do their experiments through a network of collaborations, they may have no physical lab; while a shared cause may not be essential, confidentiality agreements and software may unite them as a loose cooperative. Such approaches may become more commonplace, like the Open Innovation efforts represented by companies such as NineSigma (http://en.wikipedia.org/wiki/Ninesigma) and Innocentive (http://en.wikipedia.org/wiki/Innocentive). The One Billion Minds approach for open innovation (http://www.onebillion minds.com/) has already been mapped into the Life Sciences, where a million minds in the community have been called to participate in community annotation in Wikiproteins (http:// genomebiology.com/2008/9/5/R89). A recent example of the power of crowdsourcing is the availability of freely accessible online resources to enable and support drug discovery. For instance, online databases, including PubChem, Chemical Entities of Biological Interest or ChEBI database (http://www.ebi.ac.uk/chebi/), DrugBank (http://www.drugbank.ca/), the Human Metabolome Database (www.hmdb.ca) and ChemSpider (http://www.chem spider.com/) represent good examples (6–8) in addition to commercial databases (9) and collaborative systems like CDD (http://www.collaborativedrug.com). These represent either government or privately funded initiatives with vastly differing resources and scopes. Chemistry (and with it biology) information on the internet has thus become more accessible just as we are seeing a massive increase in screening data coming from individual laboratories. Sometimes there are synergistic benefits of crowdsourcing; for example, the efforts behind the ChemSpider platform, originally a hobby project housed from a basement and recently acquired by the Royal 1 Collaborations in Chemistry, 601 Runnymede Avenue, Jenkintown, Pennsylvania 19046, USA. 2 Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94403, USA. Department of Pharmaceutical Sciences, University of Maryland, College Park, Maryland 21201, USA. 4 Department of Pharmacology, Robert Wood Johnson Medical School, University of Medicine & Dentistry of New Jersey (UMDNJ), 675 Hoes lane, Piscataway, New Jersey 08854, USA. 5 Royal Society of Chemistry, 904 Tamaras Circle, Wake Forest, North Carolina 27587, USA. 6 To whom correspondence should be addressed. (e-mail: ekinssean @yahoo.com) Pharmaceutical Research, Vol. 27, No. 3, March 2010 (# 2010) DOI: 10.1007/s11095-010-0059-0

Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring

There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPAs ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPAs Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC-TOF/molecular feature data (match score≥90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media.

The ChEMBL database as linked open data

BackgroundMaking data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. RDF documents can natively link to related data, and others can link back using Uniform Resource Identifiers (URIs). RDF makes the data machine-readable and uses extensible vocabularies for additional information, making it easier to scale up inference and data analysis.ResultsThis paper describes recent developments in an ongoing project converting data from the ChEMBL database into RDF triples. Relative to earlier versions, this updated version of ChEMBL-RDF uses recently introduced ontologies, including CHEMINF and CiTO; exposes more information from the database; and is now available as dereferencable, linked data. To demonstrate these new features, we present novel use cases showing further integration with other web resources, including Bio2RDF, Chem2Bio2RDF, and ChemSpider, and showing the use of standard ontologies for querying.ConclusionsWe have illustrated the advantages of using open standards and ontologies to link the ChEMBL database to other databases. Using those links and the knowledge encoded in standards and ontologies, the ChEMBL-RDF resource creates a foundation for integrated semantic web cheminformatics applications, such as the presented decision support.

A Combined Atomic Force Microscopy and Computational Approach for the Structural Elucidation of Breitfussin A and B: Highly Modified Halogenated Dipeptides from Thuiaria breitfussi

The use of atomic-force microscopy (AFM) with atomic resolution shows great potential for the structural characterization of planar, proton-poor compounds, as these compounds are prone to structural corrections. [1,2] Currently, AFM has limited ability to identify element type and consequently functional groups. Additional computational techniques, such as computer-aided structure elucidation (CASE) and the calculation of 13 C NMR shifts using electronic structure calculations (DFT) may assist in this respect. Herein we show the combined use of spectroscopic methods, AFM, CASE, and DFT to solve the structures of breitfussins A and B, which could not be solved using either method alone. The subject of this study was the Arctic hydrozoan Thuiaria breitfussi (Family Sertulariidae). The few publications on the chemistry of this family show the presence of sterols, [3] polyhalogenated monoterpenes, [4] and anthracenone derivatives. [5] Arctic marine environments support highly diverse and dense populations of marine invertebrates. [6,7] A