Abstract: The digital revolution finally reached the pharmaceutical industry and machine-learning models are becoming more and more relevant for drug discovery. Often people outside the domain underestimate the complexity related to drug discovery. The hope that novel algorithms and models can remedy the challenge of finding new drugs more efficiently is often shaken when data science experts dive more deeply in the domain. Nevertheless, there are many areas in drug discovery where machine learning and data science can make a difference. A very important point is trying to better understand the data before just applying new models. Especially in early drug discovery, many data sets are very small and tricky given the data distribution, data bias, data shift or incompleteness. Another critical point are the users, to make machine learning models effective and actionable they need to be accessible and integrated into the daily work of the scientists which are most of the time not data scientists themselves. With this, an important aspect is also education of the users to deepen their knowledge and create the right expectations on machine learning models. In this presentation, several of these aspects will be discussed in more detail using examples and learnings we made over the past years.

Biography: Dr. Nadine Schneider obtained a BSc and MSc in Bioinformatics from the Saarland University in Germany. She did her PhD in Molecular Modeling in the group of Prof. Dr. Matthias Rarey at the University of Hamburg, Germany. In her PhD she worked on a novel protein-ligand scoring function which was integrated in the commercial modeling software SeeSAR (BioSolveIT GmbH). In 2014 she joined the Novartis Institutes for BioMedical Research (NIBR) in Basel (Switzerland) for a postdoc focusing on Cheminformatics and Data Science under supervision of Dr. Gregory Landrum and Dr. Nikolaus Stiefl. Since 2017 she is a researcher in the Computer-Aided Drug Design team in Global Discovery Chemistry in NIBR, Basel.