In this Webinar, Gerhard Ecker outlined computational approaches to predict the transporter interaction profile of compounds in order to minimize the risk of failures in drug development. The methods presented comprised classical machine learning models as well as deep learning approaches. The latter was also used to overcome insufficient size and imbalance of toxicity datasets. The combined use of structure-based methods for the prediction of molecular initiating events and machine learning led to a model for mitochondrial toxicity.
We also demonstrated the latest integration of StarDrop with the Phenaris Transporter Model Platform.