Publications and Presentations

Conference presentations

2020

• Chemistry ontologies and artificial intelligence, Colin Batchelor, AI for Reaction Outcome & Synthetic Route Prediction 2020, 2020

2019

• Ontologies for chemistry, Colin Batchelor, Tamara Polajnar and Richard Kidd, presented at both 2nd RSC-BMCS/RSC-CICAG Artificial Intelligence in Chemistry and AI3SD Network+ Conference 2019, 2019
• Deep learning and chemical data, Batchelor et al., Pistoia Alliance AI/ML Workshop, 2019
• Data and Data Science at the Royal Society of Chemistry, Rita Giordano, Colin Batchelor, John Boyle, presented at both IUCR Workshop on Data Science Skills in Publishing: for authors, editors and referees and European Crystallographic Computing Forum, 2019

2018

• Article-level metrics, Batchelor et al., Workshop on Open Citations, 2018 (recording available)
• Deep learning and chemical data, Batchelor et al., RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry, 2018

2017

• What Does Data Science Tell Us About Social Challenges in Scholarly Publishing? Bailey et al., Crossref LIVE17, 2017
• Advancing the chemical sciences through big data, Day et al., What can Big Data do for Chemistry?, 2017
• Using Data Science techniques to put molecules in context, Day et al., RSC CICAG group meeting - Chemical Structure Representation: What Would Dalton Do Now? 2017
• Visualizing Molecules In and Out of Context, Jeff White et al., 253rd ACS National Meeting, 2017
• Trends and Relations, Jeff White et al., 253rd ACS National Meeting, 2017
• Chemlistem - chemical named entity recognition using recurrent neural networks, Peter Corbett et al., BioCreative V.5 BeCalm Workshop, 2017

2016

• Recommender Systems for Chemistry Papers, Corbett et al., 12th German Conference on Chemoinformatics, 2016
• Integrating user behaviour, literature and cheminformatics to make molecular recommendations, Batchelor C et al., 12th German Conference on Chemoinformatics, 2016
• Evolution of open chemical information, V. Tkachenko et al., ACS, 252nd ACS National Meeting, 2016
• Building linked-data, large-scale chemistry platform: Challenges, lessons, and solutions, V. Tkachenko et al., 251st ACS National Meeting, 2016
• Not just another reaction database, A. Day et al., 251st ACS National Meeting, 2016
• Implementing chemistry platform for OpenPHACTS: Lessons learned, C Batchelor et al., 251st ACS National Meeting, 2016
• OMPOL: Visualization of large chemical spaces, P. Corbett et al., 251st ACS National Meeting, 2016

Articles

2020

• Is there a gender gap in chemical sciences scholarly communication?, A. Day, P. Corbett and J. Boyle, Chemical Science, 2020

2018

• Improving the learning of chemical-protein interactions from literature using transfer learning and specialised word embeddings., P. Corbett, 2018

2017

• Chemlistem - chemical named entity recognition using recurrent neural networks, P. Corbett and J. Boyle, Database, 2018

2015

• Chemtrove: enabling a generic eln to support chemistry through the use of transferable plug-ins and online data sources. A. Day et al., J. Chem. Inf. Model., 55 (3), 2015
• The Chemical Validation and Standardization Platform (CVSP): large-scale automated validation of chemical structure datasets. Karapetyan K et al., J. Cheminf. 7:30, 2015
• PubChemRDF: towards the semantic annotation of PubChem compound and substance databases, G. Fu et al., J. Cheminf., 2015, 7, 34.

2013

• Biology must develop its own big-data systems, Boyle J., Nature, 488(7456), 2013.
• Identification of “Known Unknowns” Utilizing Accurate Mass Data and ChemSpider. Little, J. et al., J Am. Soc. Mass Spectrom.,23(1),2013
• There and back again. Boyle J., Nature Biotechnol., 31, 2013.
• The ChEMBL database as linked open data. E. Willighagen et al., J. Cheminf. 5(23) 2013
• Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology, David P. Hill et al., BMC Genomics, 2013, 14, 513.
• First steps towards semantic descriptions of electronic laboratory notebook records, Simon J. Coles et al., J. Cheminf., 2013, 5, 52.

Book chapters

2020

• "Deep learning and chemistry data", Colin Batchelor, Peter Corbett, Aileen Day, Jeff White and John Boyle, in Artificial Intelligence in Chemistry, ed. Nathan Brown, Royal Society of Chemistry, Cambridge, in press.

2017

• "Case study: chemistry", Colin Batchelor, Peter Corbett and Simone Teufel, in Handbook of Linguistic Annotation, ed. Nancy Ide and James Pustejovsky, Springer. ISBN 978-94-024-0881-2.

2014

• "Scientific Lenses to Support Multiple Views over Linked Chemistry Data", in The Semantic Web ISWC 2014, ed. P. Mika et al., Lecture Notes in Computer Science, vol. 8796, Springer. doi:10.1007/978-3-319-11964-9_7.
• "Chemistry Ontologies", Colin Batchelor, in The Future of the History of Chemical Information, ACS Symposium Series vol. 1164, pp 219–235.

2012

• "Utilizing open source software to facilitate communication of chemistry at RSC", Aileen Day, Antony Williams, Colin Batchelor, Richard Kidd and Valery Tkachenko, in Open Source Software in Life Science Research: Practical Solutions to Common Challenges in the Pharmaceutical Industry and Beyond, Woodhead Publishing Series in Biomedicine, ISBN: 978-1-907568-97-8.