Dr Antonio de la Vega de León

BSc (Madrid), MSc (Bonn), PhD (Bonn)

Information School

Lecturer in Chemoinformatics

Dr Antonio de la Vega de León
+44 114 222 6340

Full contact details

Dr Antonio de la Vega de León
Information School
Regent Court (IS)
211 Portobello
S1 4DP

I started my research in chemoinformatics in the University of Bonn doing the MSc Life Science Informatics. For my masters thesis I developed the first multi-target activity landscape (visualizations that showed both structural and biochemical information of molecules) that could scale to large number of targets.

I did my PhD in the same university from 2012 to 2016. My thesis focused on data mining and visualization of complex chemical data sets. I also gained experience in machine learning and molecular modelling during my time in Bonn.

After I obtained my PhD, I went to the University of Sheffield as a Marie Curie postdoctoral researcher. There I worked for the EU project D3i4AD, developing multi-task machine learning models based on deep neural networks and matrix factorization techniques to support phenotypic screening campaigns.

I became a Lecturer in Chemoinformatics in the Information School in October 2018.

Research interests

Computational techniques to support decision making are becoming more prevalent in chemical and pharmacological research. I am interested in different aspects of how these techniques can contribute to drug discovery research:

  • Machine learning model interpretation: machine learning models create associations between structural elements of sets of molecules and biochemical properties that are expensive to measure. However, in many cases it is not possible to know what these associations are. I am interested in techniques that are able to make these models more understandable.
  • Visualization of chemical space: visualization techniques are a useful way to condense a large amount of information in an understandable fashion. I am interested in developing novel visualizations that bridge the structural information of sets of molecules to their physico-chemical and biochemical properties.

Journal articles


Conference proceedings papers

  • Bates J, Cameron D, Checco A, Clough P, Hopfgartner F, Mazumdar S, Sbaffi L, Stordy P & de la Vega de León A (2020) Integrating fate/critical data studies into data science curricula: Where are we going and how do we get there?. FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp 425-435) RIS download Bibtex download
  • de Leon ADLV & Gillet V (2018) Phenotypic screening aided by multitask prediction methods. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, Vol. 256 RIS download Bibtex download
  • de Leon ADLV & Gillet V (2017) Using deep neural networks with heterogeneous chemical data to support phenotypic assay campaigns. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, Vol. 254 RIS download Bibtex download
Research group

Current PhD students

  • Moritz Walter: Understanding artificial intelligence models for toxicity prediction.
Teaching interests

I currently contribute to the ‘Data mining and visualization’, ‘Data analysis’, ‘Database design’ and ‘Designing usable webpages’ modules available to MSc students.