Miguel García-Ortegón

This paper uses transfer learning to identify potent antibacterials from large chemical libraries, finding 54% of tested compounds active against E. coli with sub-micromolar potency.

This paper uses conditional neural processes to predict adverse drug reactions from sparse data and finds they are well-calibrated and effective across organ systems.

This study develops public classifiers for predicting drug-induced cardiotoxicity using FDA DICTrank data, identifying key chemical and biological features that distinguish toxic from non-toxic compounds.