xenobiotic molecules that were either found in collateral experiment designs in literature or as putative compounds in pathway databases. We first suggest a paradigm change in publishing metabolite information and subsequently explain how such change would alleviate the problems listed above. Small molecule reports need to be annotated by PubChem CIDs and structures Where extensive data on small molecules are presented in chemical reports, structures should be annotated according to compound identifiers in the authoritative database PubChem. In case structures are not yet included in PubChem, these should be encoded in a chemical structure to allow database search requests and chemical overlap calculations. The best way to represent a chemical compound is not by a name or even a database identifier, but by its structure encoded in Structure Data Format (SDF MDL V2000) or the open Chemical Markup Language (CML) format or InChI codes. A few databases already provide the IUPAC/NIST standard of InChI codes [50] or the shorter hashed InChIKey [51]. The new InChIKey resolver services [52] implemented by the Royal Society of Chemistry (RSC) [53] and Chemspider [28] allows to create InChIKeys from molecular structures and a reverse lookup of InChIKeys to obtain the associated known structures from molecular databases. The InChIKey can be used for web based literature search and also for chemical database search and merging of compound lists from multiple sources [54]. Some other databases support the SMILES code for structures. The use of SMILES code is not recommended because multiple vendors create different representations of the SMILES code. Also true canonical (unique) SMILES are vendor specific. The machine-readable structure hit lists could in principle be used to obtain a library containing chemical connectivity information and spectral meta-data. The Metabolomics Standards Initiative (MSI) suggests reporting metabolites using unique chemical identifiers like InChI Codes or PubChem compound IDs [55]. Both the MSI and the broader community initiative MIBBI (Minimum Infor- mation for Biological and Biomedical Investigations) [56,57] lack the power to enforce requirements to report and publish metabolite data and metadata (e.g. NMR and MS spectra) in electronic formats but are good-will projects with recommenda- tions and lead-by-example standards. The same can be said about the Blue Obelisk movement which supports interoperability in Data Exchange in Chemistry chemical informatics by promoting reusable chemistry via open source, open data, and open standards [58]. In addition, spectral data could be added to PubChem identifiers using the open XML spectral exchange format CMLSpect [72] or open source tools (project SPECTRa) [73]. However, at this point, spectral data depositions in open exchange formats cannot be made mandatory, as some of the open exchange formats cannot handle complex multistage chromatographic data sets, such as LC-MS with MS2 and MS3 scans and ion trees. In such a case the submission of the raw files in vendor specific formats seems to be sufficient. Small molecule reports need to be public and machine- readable Chemical structures and spectra should be made available in machine readable formats and provided by either the submitting authors or the publisher. Journals should ensure data submission before accepting manuscripts and funding agencies should require submission of metabolite metadata in their funding guidelines. The Royal Society of Chemistry (RSC) with Project Prospect [74] and two Nature journals have shown that substance and ontology annotations can be done successfully. The current approaches to compile chemical knowledge repositories use manually and automated full-text curation and optical character recognition to obtain structures and spectra from graphics and text, as in the Beilstein Handbook of Chemistry from 1881 and the Chemical Abstracts Service starting in 1907 [61,62]. The current approach is shown in Figure 3. Experimental data are digitally recorded in a laboratory and condensed into a paper publication. During that process, attention is focused to the main point of a scientific paper, mostly by excluding data and reducing noise. A major problem is that data is also removed which might be interesting for other researchers in a different context, e.g. metabolic concentrations that do not change after a perturbation. Results are published on paper or electronic (bitmap PDF) documents, hence transforming digital into analog data. If metabolite structures and spectra are converted back from analog into digital form, optical structure recognition and text to structure algorithms are used which are very error prone. In some cases authors are already required to submit machine readable structures and spectral data directly to the supplement section of a journal. In such a case publishers should not transform machine readable data back to bitmap PDF and destroy the semantic content. That is especially important for data tables, chemical reaction drawings and high resolution spectra in supplemental data. Additionally to the provided Figure 3. Data loss occurs during conversion of digital data in the lab to analog data in a publication. Later analog information from a publication including structures and molecular spectra are converted back to digital information (hamburger-to-cow algorithm). Such name to structure and OCR-optical chemical structure recognition algorithms are error prone and information loss is even higher for complex molecular spectra. Direct electronic submission of chemical structures and spectral data is recommended. doi:10.1371/journal.pone.0005440.g003 PLoS ONE | www.plosone.org 6 May 2009 | Volume 4 | Issue 5 | e5440