Head of Chemistry Services
Dr Leach holds BA and DPhil degrees in Chemistry from Oxford University. He joined EMBL-EBI in August 2016 from GSK Research and Development, where he enjoyed a successful career since 1994, most recently as Global Head of Biomolecular Sciences. He also served as a Trustee of the Cambridge Crystallographic Data Centre from 2006-2015 and was editor of the Journal of Computer-Aided Molecular Design from 1997-2012.
arl [at] ebi.ac.uk
ORCID iD: 0000-0001-8178-0253
Tel:+ 44 (0) 1223 49 4333 / Fax:
We develop and manage ChEMBL, EMBL-EBI’s database of quantitative small-molecule bioactivity data focussed in the area of drug discovery. ChEMBL stores curated two-dimensional chemical structures and abstracted quantitative bioactivity data alongside calculated molecular properties. The majority of the ChEMBL data is derived by manual abstraction and curation from the primary scientific literature, and it covers a significant fraction of the published structure–activity relationship (SAR) data in drug discovery. ChEMBL is widely used by academia, industry and not-for-profit organisations to tackle many problems related to drug discovery.
We also produce SureChEMBL, for patent information, and UniChem, which provides a convenient way to cross-reference multiple databases using chemical structure. ChEBI is a database which contains information on small molecules relevant to biology and is widely used by other bioinformatics resources.
In addition to delivering these resources to the community, we are involved in various other collaborative projects that build upon our capabilities and expertise. Currently these include Open Targets and Illuminating the Druggable Genome, both of which are concerned with target selection and prioritisation. We also have a significant current focus on safety and toxicity via IMI-eTRANSAFE, IMI-TransQST and EU-ToxRisk.
Our research interests are broadly in the areas of molecular recognition and drug discovery. We use computational methodologies to understand at a molecular level a wide variety of biological processes and phenomena. We develop predictive models that can be used to design new molecules in silico. We seek to understand what factors contribute to success and failure in drug discovery. A significant proportion of our work is done in collaboration with other drug discovery scientists from academia and industry. Current projects include the use of computer simulations to predict organ transplant outcomes, using machine learning to predict bioactivity profiles, target tractability prediction, structure-based drug discovery, and the use of text mining to identify bioactivity data from a variety of sources.