Deep Learning has been applied in many aspects of the drug discovery process and to many different types of data. Typically, two stages are involved: 1) identify the best target for a given disease indication; 2) identify the best small molecule drug for that target. Both steps are extremely labor intensive due to the enormous search space. Prediction algorithms trained on large amounts of data hold the promise of accelerating the search for better targets and better molecules.
In the upcoming heidelberg.ai event, Tobias Sikosek will tell us about this topic by selecting a few highlights from the very recent literature in this field. Tobias holds a PhD in biocomputing and organizes the Biocomputing Meetup Heidelberg (). After doing a postdoc at the university of toronto he is now working as a Senior Data Scientist at a major pharmaceutical company.
Accelerating Drug Discovery, speaker: Tobias Sikosek