Rocío Mercado Oropeza | Assistant Professor at Chalmers University of Technology

Abstract: Generative and predictive machine learning models are reshaping how we explore chemical compound space, yet meaningful impact requires models that can reason across molecular scales, handle heterogeneous data, and adapt to real-world design constraints. In this talk, I will present recent work from our group on AI-driven molecular design, spanning small-molecule therapeutics, targeted protein degraders, and sustainable materials for emerging technologies. A central focus of this talk will be on synthesis and biological fate: I will discuss RetroSynFormer, a Decision Transformer for multi-step retrosynthesis planning, as well as LAGOM, a transformer-based chemical language model for predicting drug metabolites. The models we develop illustrate how AI can support chemists not just in designing molecules, but in planning how to make them and anticipating what happens to them in a biological system. More broadly, we are developing approaches that combine generative modeling, structure-aware learning, and chemically informed representations to support molecular design tasks across various length scales and domains. Our work is carried out in close collaboration with industrial partners across pharmaceuticals and advanced materials (including AstraZeneca, Intel, and Merck), ensuring that the methods we develop translate to practical design workflows.

About the speaker: I am a tenure-track assistant professor in the Data Science and AI division at Chalmers since January 2023. I head the AI Laboratory for Molecular Engineering (AIME) in the Department of Computer Science and Engineering. If you are a student/postdoc interested in doing research at the interface of machine learning, life science, and materials science in a vibrant and supportive environment, feel free to reach out via email to learn more about upcoming opportunities in my team and at Chalmers!

Previously, I was a postdoctoral associate in the Coley group at MIT, as well as an industrial postdoc in the Molecular AI team at AstraZeneca. Throughout my postdoctoral career, I worked on the development of Generative AI for small molecule drug discovery. Before AstraZeneca, I was a PhD student in Professor Berend Smit’s molecular simulation group at UC Berkeley and EPFL where I worked on materials discovery for carbon capture applications. I received my PhD in Chemistry from UC Berkeley in August 2018, and my BS in Chemistry from Caltech in June 2013.

I am Mexican-American, originally from Wilmington, California, but I fell in love with Gothenburg when I moved here for my first postdoc! I am also vegan.

Event recording