D298 Nucleic Acids Research, 2009, Vol. 37, Database issue toxicity information (Figure 1, II-B): dose testing type (e.g. LC50) organism or cell line, the compound was tested on a two-dimensional structure a three-dimensional visualization of the structure For each compound in the result table, a separate ‘Detailed View’ window (Figure 1, II-A) is provided, dis- playing further structural and chemical properties, such as number of rings, atoms, H-bond donors and acceptors, rotatable bonds, SMILES, logP and molecular weight. All these information can be downloaded in PDF format, together with the atomic coordinates in MOL format. Some of the similar compounds, found for Hit1, like Alfacalcidol (Figure 1 II, red boxes), for example, are very toxic. This ﬁnding suggests that despite all the other similar compounds found, which are only slightly toxic, the candidate itself might be tested for toxicity start- ing with very low concentrations. Another useful feature of SuperToxic, is the user upload interface, which allows the scientiﬁc community to contri- bute to the database. There are several data required for the upload: the structure, toxicological information (type of toxicity, dose, unit, organism or cell line) and an email address for further correspondence. After manual cura- tion, the database will be updated with the new compound. METHODS Data mining SuperToxic was established on the basis of data from the publicly available databases PubMed, DSSTox (15) and NCI60 (16). Furthermore, the book ‘Biogene Gifte’ (17), was manually surveyed, making the data available online for the ﬁrst time. Toxicity data were collected from litera- ture by extensive text mining. A searchable index from the PubMed database was built. In the next step, the index was ﬁltered for toxicity-related keywords and various pat- terns, like units or IUPAC names. Finally, all relevant text passages were manually curated in regard to the presence of any toxicity information. The data from all sources were merged to eliminate duplicated compounds. The database currently contains about 60 000 compounds. Also included, are references to the origin of the compounds and about 600 entries in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (18). To detect potential targets in biochemical pathways approximately 400 compounds were detected in the Protein Data Bank (PDB) (19,20) and the corresponding targets were linked via more than 800 KEGG and 3600 UniProt (21) entries. Calculation of chemical properties The calculations of chemical properties, e.g. molecular weight and number of H-bond donors and acceptors, were performed with functions from OpenBabel 2.1, an open source chemical toolbox (22) (http://openbabel. sourceforge.net/). To compute the properties for the struc- tures, the MyChem extension (an implementation of the OpenBabel 2.1 library for MySQL) was used (http:// mychem.sourceforge.net/). Analysis of chemical and structural properties For the complete dataset, the distributions of molecular weight, LogP and H-bond donors and acceptors were ana- lyzed, whereas drugs (23) and natural compounds (24) served as reference groups. A reduced dataset, derived from the NCI60 panel, was subdivided into three toxicity groups, represented by log (LC50): the slightly toxic, medium toxic and highly toxic compounds. For each group, the distribution of chemical properties was calculated separately, to reveal interdependencies regarding the toxicity. The results are shown under ‘Statistics’ on the SuperToxic website. Structural fingerprint The similarity search is performed, using so-called struc- tural ﬁngerprints, a binary string with a length of 1536 bits, which encodes for the chemical characteristics of a compound. Within this database, a combination of two ﬁngerprints was used: (i) a 1024 bit ﬁngerprint based on MDL; (ii) a 512 bit ﬁngerprint encoding for 317 structural properties deﬁned as SMARTS pattern (http://www. daylight.com/dayhtml/doc/theory/theory.smarts.html), pro- vided by OpenBabel (http://openbabel.org/wiki/FP2). The ﬁrst one generates a ﬁngerprint for each chemical structure, the second provides for exact structural pat- terns. The application of this combined ﬁngerprint leads to an improvement of the similarity and substructure searches and yields in more detailed results. The ﬁnger- prints of all compounds were precalculated and stored in the database. Similarity search During the similarity search, the ﬁngerprint of the input structure is built and compared with the ﬁngerprints of the database entries using the Tanimoto coeﬃcient. It is a similarity index and deﬁned as: Nab T ¼ Na þ Nb Nab Na and Nb describe the number of bits, set to 1 in the ﬁngerprint, of compound a and b, respectively. Nab is the number of bit positions set to 1 in both ﬁngerprints. A molecule with a Tanimoto coeﬃcient >0.85 to an active compound is assumed to be biologically active itself (25). The Tanimoto calculations are performed using MyChem. Substructure search For the substructure search, the database entries are ﬁl- tered, according to the number of atoms and rings. Thus, structures with less atoms or rings, compared with the query molecule, are not considered narrowing down the search space. Afterwards, the ﬁngerprint of the query