Abstract

Recent technological advancement in the field of health science brought with it a deluge of both chemical and biological data that is in need of interpretation. This information can be used to generate better understanding of drivers of disease – such as chemical, biological and/or genetic markers – but possibly even more importantly it can be used to design more efficacious and safe medicines prospectively. Still, currently data is often not used effectively to reach this goal, partly due to technical reasons (the sheer amount of data), but even more fundamentally also due to lack of data integration (e.g. inconsistent identifiers), and our often crude understanding of what in particular biological readouts actually mean in order to make better decisions based on them.

In our research group, which currently comprises about 20 members, we aim to address the above point by integrating data from across the chemical and biological domains in order to improve decision making in the drug discovery process. To this end, project-specific data is employed (often in collaboration with pharmaceutical companies, such as AstraZeneca, Johnson&Johnson, Eli Lilly, BASF , Unilever, Aboca, and others) in order to address one of two principal aims:

Firstly, we aim to decide which compound is most likely to possess desired efficacy and the relatively best side effect profile, given the data at hand.

Secondly, we aim to understand the mode-of-action, or more generally biomodulatory capabilities, of a small molecule by integrating diverse data (such as on-target bioactivity data and RNA -Seq data, but also others).

Given a large number of collaborations with both academic groups as well as pharmaceutical, chemical, and consumer goods companies we have accumulated significant expertise in the area of chemical and biological data integration in order to support decision making in the drug discovery process in the above two areas (but also beyond).

Our presentation will conceptually describe which types of data we are currently integrating, our plans for future projects, as well as case studies where ‘Big Data’ from across the chemical and biological domains was able to improve compound design, as well as enhance our understanding of a more complete bioactivity profile of chemical structures.