Research

Generating molecules from scratch with bespoke properties is arguably one of the most challenging tasks in chemistry. In the past few years, generative deep learning has remarkably enhanced the field of de novo design, by allowing to generate novel bioactive molecules from scratch, without the need of human-engineered features and assembly rules. By learning from the underlying data distribution, methods like Recurrent Neural Networks (RNNs) can be used to generate novel molecules from scratch that possess the desired bioactivity profile. The Molecular ML group is currently active in boosting the potential of generative deep learning to support medicinal chemistry and drug discovery.

Optimizing metrics and boosting model performance is something we have a lot of fun with. Ultimately, however, what we want to achieve is to bring the models to the wet-lab. This allows us to verify whether our computational hypotheses are correct, and if the AI approaches are applicable in real-world scenarios. Also, we are really into collaborating with experts in chemical biology and drug discovery, to ultimately tackle unsolved challenges with AI.