In Silico Transporter Modeling and its Role in Computational Toxicology
This webinar was presented by Gerhard Ecker, Bailey Montefiore and Matt Segall.
In this webinar, we outline computational approaches to predict the transporter interaction profile of compounds in order to minimize the risk of failures in drug development. Methods presented comprise 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 demonstrate the latest integration of StarDrop with the Phenaris Transporter Model Platform. You can find additional information on this script, including a download link on its dedicated Community page (download requires free registration to our Community section).