Development and Interpretation of Machine Learning Models for Drug Discovery.

In drug discovery, domain experts from different fields such as medicinal chemistry, biology, and computer science often collaborate to develop novel pharmaceutical agents. Computational models developed in this process must be correct and reliable, but at the same time interpretable. Their findings have to be accessible by experts from other fields than computer science to validate and improve them with domain knowledge. Only if this is the case, the interdisciplinary teams are able to communicate their scientific results both precisely and intuitively. This work is concerned with the development and interpretation of machine learning models for drug discovery. To this end, it describes the design and application of computational models for specialized use cases, such as compound profiling and hit expansion. Novel insights into machine learning for ligand-based virtual screening are presented, and limitations in the modeling of compound potency values are highlighted. It is shown that compound activity can be predicted based on high-dimensional target profiles, without the presence of molecular structures. Moreover, support vector regression for potency prediction is carefully analyzed, and a systematic misprediction of highly potent ligands is discovered. Furthermore, a key aspect is the interpretation and chemically accessible representation of the models. Therefore, this thesis focuses especially on methods to better understand and communicate modeling results. To this end, two interactive visualizations for the assessment of näıve Bayes and support vector machine models on molecular fingerprints are presented. These visual representations of virtual screening models are designed to provide an intuitive chemical interpretation of the results.