Egon Willighagen

Biography

Egon Willighagen studied Chemistry at the University of Nijmegen in the Netherlands (1993-2001) where he did a minor in organic chemistry at the Department of Supramolecular Chemistry (Prof. R. Nolte) studying the supramolecular agglomeration of amphiphiles with relation to their DNA transfection properties, and a major in chemometrics at the Deparment of Analytical Chemistry (Prof. L.M.C. Buydens) studying the unsupervised classification of polymorphic organic crystal structures.

He continued his studies on the relation of representation of molecular knowledge and machine learning during his PhD at the same Department of Analytical Chemistry at now-called Radboud University (2002-2006). Taking advantage of his extra-curriculum research in cheminformatics, he worked on on methods to optimize the amount of information gained from pattern recognition methods in the fields of Quantitative Structure-Activity Relationships (QSAR), supervised clustering and prediction of properties of organic crystal structures, the general reduction of error introduced in exchange of chemical data, and on improving the reproducibility of data analysis in this field in general. This research was partly performed at the European Bioinformatics Institute in Hinxton (Prof. J. Thornton) and the nearby Cambridge University in the United Kingdom (Prof. P. Murray-Rust, 2003, 3 months). The work resulted in the thesis Representation of Molecules and Molecular Systems in Data Analysis and Modeling (2008, ISBN:978-90-9022806-8).

After his PhD research he continued his efforts on reducing the error introduced by data aggregation and cheminformatics toolkits during a year at the Cologne University Bioinformatics Institute in Germany (dr C. Steinbeck) (2006-2007), participating the bioinformatics education (Homology Modeling). He then returned to The Netherlands and did a year at Wageningen University and Research Centre embedded in the Netherlands Metabolomics Center and worked on use of cheminformatics in accurate metabolite identification (2007-2008).

In 2008 Willighagen started a post-doc at the Department of Pharmaceutical Biosciences (Prof. E. Brittebo) strenghtening the cheminformatics knowledge of the Bioclipse and Proteochemometrics Group (Prof. J. Wikberg) at Uppsala University. His research here focused on linking his cheminformatics research with pharmaceutical and drug discovery research. The goal here is to make the data modeling more insightful, by upscaling of data analysis by using e-Science approaches and the use of semantic markup languages and ontologies which make it possible to link statistical models to external, complementary data sources. This allows linking of patterns between data types and between domains.

After a post-doc in the area of text mining for chemistry back at the Unilever Center (Prof. P. Murray-Rust) in the last three months of 2010, Willighagen started in 2011 a post-doc at the Institute of Environmental Medicine of the Karolinska Intitute (Prof. B. Fadeel, and Prof. R. Grafström) working on applications of cheminformatics in toxicology.

In 2012 Willighagen returned to the Netherlands, and started a post-doc position at Maastricht University, to work on the EU/IMI project Open PHACTS. He acquired the 4M euro project eNanoMapper (with 840k euro to fund research in Maastricht) and started as assistant professor in the same group in 2014, and got tenure in 2017.

Group Members

• Denise Slenter (Maastricht University; Researcher 2017-; ORCID:0000-0001-8449-1318)

• Ammar Ammar (Maastricht University; Researcher 2020-; ORCID:0000-0002-8399-8990)

• Jeaphianne van Rijn (Maastricht University; Researcher 2020-; ORCID:0000-0001-5026-7705)

• Javier Millán Acosta (Maastricht University; Researcher 2022-)

• Ozan Cinar (Maastricht University; Researcher 2022-)

But also co-supervising the PhD candidates Jente Houweling and Marvin Martens.

Past Members

• Helena Basaric (Maastricht University; Research Software Engineer 2022-2023)

• Laurent Winckers (Maastricht University; Researcher 2019-2020; ORCID:0000-0002-9454-4783)

• Serena Bonaretti (Maastricht University; Researcher 2019; ORCID:0000-0003-4264-1773)

• Friederike Ehrhart (Maastricht University; Researcher 2015-2017; ORCID:0000-0002-7770-620X)

• Linda Rieswijk (Maastricht University; Researcher 2015-2017; ORCID:0000-0002-6106-1347)

• Bart Smeets (Maastricht University; PhD student 2014-2016; ORCID:0000-0001-6370-6965)

• Cristian Munteanu (Maastricht University; postdoc 2014; ORCID:0000-0002-5628-2268)

Past Undergraduate and Master Students

• Jacob Windsor (Maastricht University; B.Sc. student 2017; report:doi:10.6084/m9.figshare.5027888.v1)

• Vincent Couckuyt (Maastricht University; B.Sc. student 2016)

• Miguel Correa (Maastricht University; M.Sc. student 2015; from Wageningen University)

• Esmee Eeltink (Maastricht University; B.Sc. student 2014; from the University of Amsterdam)

• Patricia Zaandam (Maastricht University; B.Sc. student 2014; blog;)

• Ann-Sofie Andersson (Uppsala University; M.Sc. student 2010; blog; report:urn:nbn:se:uu:diva-155184)

• Samuel Lampa (Uppsala University, M.Sc. student 2009-2010; blog; report:urn:nbn:se:uu:diva-146738)

Other students that I supervised include Harm (6 month M.Sc. partical University of Nijmegen), Niels Out and Rob Schellhorn (3 month Programmeerzomer projects), and Alexandr Goncearenco, Rianne Feijten, Answesha Dutta, and Manas Awashti (3 month Google Summer of Code projects).

Semantic Technologies

Technologies studied here include markup languages, like Chemical Markup Language, and the family of semantic approaches around the Resource Description Framework. These methods are being studied for use in cheminformatics and exchange of molecular data. This work has resulted in the development of CMLRSS, QSAR-ML, the Blue Obelisk Descriptor Ontology (BODO), and the CHEMINF ontology.

Molecular Representation

This branch of the research focuses on the presentation of molecular data, such that statistical methods can extract the most information from the data or generate the best prediction models. This research resulted in the development of a new descriptor for molecular crystal structures, the debunking of NMR spectra for some modeling approaches, and the development of applied tools, like the Chemistry Development Kit.

Statistics

Statistical methods help us find, understand and visualize complex patterns in our molecular data. This research focuses on improving the expert validation of our statistical models, by linking the models to external data. The latter brings us back to the semantic technologies to do that accurately. This work resulted in the application of supervised Self-Organizing Maps to classification problems with multiple end points.

Applied Cheminformatics

This part of the research is about getting the above methods used in other scientfic fields. This involves the development of tools that (unfortunately) hide much of the research from the above three fields, so that they can be easily used in other research fields. This research, particularly, is well accepted by the scientific community. Solutions here include the Chemistry Development Kit, Jmol, Bioclipse, and Oscar, which all make more fundamental research in Molecular Chemometrics more accessible.