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Annual Scientific Report 2015

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containing the mapping between compound structures<br />

and the patents they appear in. We also developed a<br />

data client feed that enables users to maintain a regular<br />

stream of the patent data behind a firewall and integrate<br />

the data with their in-house data. Using SciBite’s<br />

Termite software, we annotated the patent corpus<br />

with dictionaries and ontologies for biological terms<br />

including gene and diseases. The mapping is available<br />

through the OpenPHACTS API.<br />

In <strong>2015</strong> we intensified our work on the annotation<br />

of marketed drugs and clinical candidates with their<br />

intended therapeutic targets and diseases. This provides<br />

a rich source of data for researchers interested in<br />

validating therapeutic targets and identifying novel<br />

ones. This was carried out as part of the NIH-funded<br />

Illuminating the Druggable Genome (IDG) project and n<br />

Open Targets (formerly CTTV) project.<br />

We continued to participate in two EU-funded<br />

projects, eTOX and HeCaTos, which aim to better<br />

identify and curate toxicity data and apply it to the<br />

prediction of toxicological endpoints. We continued<br />

our work on OpenPHACTS, an Innovative Medicines<br />

Initiative project that integrates pharmacological<br />

data across diverse resources. We also participated<br />

in the IDG project, Open Targets and Corbel, the<br />

European infrastructure project that follows on from<br />

BioMedBridges.<br />

Research<br />

We developed methods to predict molecular targets<br />

and off-targets using structural information from<br />

phenotypic screening data, an essential step in lead<br />

optimisation, polypharmacology and the study of side<br />

effects. Through collaborations, we validated three Mtb<br />

targets from our predictions for potential tuberculosis<br />

drug leads using biochemical and biophysical methods,<br />

and from the generation of target-ligand structures. In<br />

addition to developing a general theoretical model of<br />

drug resistance and drug combinations, we performed<br />

extensive data mining to retrieve information on<br />

the relationship between the type of target and the<br />

physicochemical properties of antibiotics – information<br />

that is important for the development of new drugs.<br />

John Overington<br />

Chemogenomics<br />

BSc Chemistry, Bath. PhD in Crystallography,<br />

Birkbeck College, London, 1991. Postdoctoral<br />

research, ICRF, 1990-1992. Pfizer 1992-2000.<br />

Inpharmatica 2000-2008.<br />

At EMBL-EBI from 2008 to <strong>2015</strong>.<br />

Anne Hersey<br />

Acting Team Leader, ChEMBL<br />

BSc Chemistry, University of Kent, PhD in<br />

Physical Chemistry, University of Kent, 1982,<br />

GlaxoSmithKline and former companies1982-<br />

2009.<br />

At EMBL-EBI since 2009.<br />

Future projects and goals<br />

In 2016 we will continue to broaden the utility and<br />

content of ChEMBL and SureChEMBL by adding<br />

additional annotation, for example on diseases, targets<br />

and data measured using genetic variants of proteins.<br />

We will expand our use of ontologies to increase<br />

indexing of the ChEMBL data, particularly for complex<br />

and high-value endpoints such as ADMET, and in vivo<br />

pharmacology assays. We will develop technologies<br />

that enable us to build curation and data submission<br />

interfaces in a flexible and extendable way, and use<br />

text-mining methodologies to identify journal articles<br />

that enhance our coverage of chemical space. We will<br />

continue to develop automation methods for ChEMBL<br />

to enable the database to be updated more regularly<br />

and simply. We will also develop a sub-structure and<br />

similarity search facility for UniChem.<br />

Selected publications<br />

Davies M, et al. (<strong>2015</strong>) ChEMBL web services:<br />

streamlining access to drug discovery data and utilities.<br />

Nucleic Acids Res. 43: W612-W620<br />

Gaulton A, et al. (<strong>2015</strong>) A large-scale crop protection<br />

bioassay data set. <strong>Scientific</strong> Data 2:150032<br />

Mugumbate G, et al. (<strong>2015</strong>) Mycobacterial dihydrofolate<br />

reductase inhibitors identified using chemogenomic<br />

methods and in vitro validation. PLoS One 10:e0121492<br />

Papadatos G, et al. (2016) SureChEMBL: a large-scale,<br />

chemically annotated patent document database Nucleic<br />

Acids Res. 44:D1220-D1228<br />

Papadatos G, (<strong>2015</strong>) Activity, assay and target data<br />

curation and quality in the ChEMBL database. J.<br />

Computer-Aided Mol. Design 29:885-896<br />

<strong>2015</strong> EMBL-EBI <strong>Annual</strong> <strong>Scientific</strong> <strong>Report</strong> 112

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