Drug Discovery is an essential endeavor to tackle threats to human health. Notwithstanding, the development and subsequent market penetration of new pharmaceuticals is a critical yet time-consuming and expensive process. To address shortcomings in this process, new approaches have been explored to combine both experimental and computational routes. In particular, as an in-silico approach, virtual screening is proposed as an alternative to identify active molecules towards therapeutic biological targets. Recent advances in artificial intelligence (AI) have provided more effective search algorithms while reducing the time it takes to do virtual screening and implementing new therapeutic candidates.

In this research line, we employ different state-of-the-art techniques of deep learning (specifically Natural Language Processing (NLP), Artificial Vision, and Graph Networks) to model relationships between molecules, proteins, and the variables involved in the task of predicting the affinities among ligands and targets.

This project studies the use of generative deep learning models for business process simulation. The main hypothesis is that it is possible to train generative models from business process execution logs using deep learning techniques, which can replicate the behavior of the business process more accurately than existing business process simulation approaches. In addition to exploring this hypothesis, the project investigates how to use and modify such generative models for “what if” simulation based on business process changes.