Proefschrift

The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment

This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to find relations in protein-ligand bioactivity data and then predict using a virtual screen whether compounds that had never been tested on a particular protein, or set of proteins.

Auteur

B.J. Bongers

Datum

08 mei 2024

Links

Thesis in Leiden Repository

With this, sets of compounds were suggested for experimental validation that were significant in a myriad of ways. Another study investigated the mutational patterns in cancer, applying a large dataset of mutation data and identifying several motifs in G protein-coupled receptors. The thesis also contains the work done on the Papyrus dataset, a large scale bioactivity dataset that focuses on standardising data for computational drug discovery and providing an out-of-the-box set that can be used in a variety of settings.

• affinity prediction
• anion transporter
• artificial intelligence
• big data
• computational modeling
• machine learning
• proteochemometrics
• somatic models