"Machine learning in chemistry and beyond" (ChE-651) seminar by Rocío Mercado "Exploring new frontiers in drug discovery using deep generative models"


Event details

Date 08.11.2022
Hour 15:1516:15
Speaker Rocío Mercado is currently a post-doc at MIT working with Professor Connor Coley.  Previously, she completed an industrial post-doc at AstraZeneca in the Molecular AI team where she worked on graph molecular generative models for small molecule drug design. Before that, she completed a PhD in Chemistry with Professor Berend Smit at UC Berkeley and EPFL in molecular simulation. Rocío will be starting an assistant professorship at Chalmers University of Technology in the Data Science and AI division working on data-driven molecular design.
Location Online
Category Conferences - Seminars
Event Language English

Artificial intelligence (AI) is transforming our approach to biomolecular engineering. In the drug discovery sector, the development of generative and predictive tools that can quickly learn from biochemical data is pushing the frontiers of how we discover and repurpose drugs through de novo molecular design. De novo design – the concept of designing molecules with desired properties from scratch so as to minimize experimental screening – is poised to allow scientists to more efficiently traverse chemical space in search of optimal molecules, and delegate error-prone decisions to computers via the use of predictive and generative computational models. In drug development, de novo design methods can aid medicinal chemists in the design and selection of drug candidates, with the added advantage that they can learn from datasets of billions of molecules in minutes and be constantly updated with new data. In this talk I will introduce molecular deep generative models (DGMs) and their utility in de novo design. DGMs use deep neural networks to build new molecules in silico, and work by proposing node-by-node modifications to an initial graph structure to generate compounds predicted to achieve a certain property profile. Such models can be applied to a range of therapeutic modalities; here I will focus on the design of small molecule protein binders and heterobifunctional degraders. I will end by touching on the importance of interdisciplinary communication for the development of new advances in this field, as well as discussing the importance and impact of open-source work.

Practical information

  • General public
  • Free


  • Kevin Maik Jablonka, Solène Oberli, Puck van Gerwen, Andres M Bran, Jeff Guo, Philippe Schwaller


  • Kevin Maik Jablonka, Solène Oberli, Puck van Gerwen, Andres M Bran, Jeff Guo, Philippe Schwaller