Seminar by Derek van Tilborg: "Molecular deep learning at the edge of chemical space"

Molecular machine learning models often fail to generalize beyond the chemical space of their training data, limiting their ability to reliably perform predictions on structurally novel bioactive molecules. To advance the ability of machine learning to go beyond the ‘edge’ of their training chemical space, we introduce a joint modeling approach that combines molecular property prediction with molecular reconstruction, enabling us to estimate model generalizability through a new reconstruction-based ‘unfamiliarity’ metric. Via a systematic analysis spanning more than 30 bioactivity datasets, we demonstrate that unfamiliarity not only effectively identifies out-of-distribution molecules but also serves as a reliable predictor of classifier performance. We show that our method is independent from well-established methods to estimate prediction reliability and that we can successfully identify high-confidence predictions on molecules that are structurally novel – a central challenge in drug discovery. Our findings highlight that joint modelling can be a powerful strategy for extending the reach of machine learning models into uncharted regions of chemical space, advancing the discovery of diverse and novel molecules.

Derek van Tilborg is a PhD student in the molecular machine learning team of Francesca Grisoni at the Eindhoven University of Technology (Chemical Biology, Dept. Biomedical Engineering). Having an academic background in both biomedical sciences and bioinformatics, he has developed a passion for artificial intelligence in the realm of drug discovery. Derek works on improving how machine learning approaches are applied to drug screening data to bridge the gap between computational methods and experiments. His research interests are focussed on solving the practical bottlenecks that accompany prospective studies, such as the implementation of active learning and handling out-of-distribution molecular data.