Artificial Intelligence for Natural Product Drug Discovery

Lorentz Center Chemistry competition
This workshop is winner of the Chemistry competition 2020. For more information see our Lorentz Center Chemistry workshop competition.

Plants, fungi and bacteria produce a wide range of specialized metabolites, also known as natural products. Natural products area promising source for drug discovery, and many antibiotics, chemotherapeutics and immunosuppressants derive from them. While their use diminished between roughly 2000 and 2010, recently, natural product discovery has seen a renaissance in both academia and small biotech start-ups, catalyzed by large-scale omics data that allows a significantly deeper access to the hidden chemical treasure troves of the biosphere.

In parallel, artificial intelligence approaches have also led to exciting developments in the computational drug design field, facilitating prediction of biological activities and de novo design of drugs for certain molecular targets.

The aim of this workshop is to exchange ideas and experiences between the fields of omics-based natural product discovery and computational drug design, to jointly explore the design of new machine learning methodologies and how they could be applied to these fields, and to make concrete plans for connecting and integrating approaches from the two fields to achieve new synergy.