Prediction and uncertainty quantification of small molecule bioactivity from chemical structure and phenotypic data KU Leuven
Due to its diversity and increasing availability, in-vitro assay data can be a valuable source for predicting bioactivities, monitoring side effects, and repurposing drugs. Machine learning methods have been proven to be exceptionally useful tools for exploiting this information in order to find small molecular ligands with a desirable activity profile. Macau is a Bayesian matrix factorization model that was previously developed at KU Leuven ...