In this presentation, I will provide a concise overview of my current research focused on molecular design in the small data regime. Specifically, I will discuss the importance of developing reliable predictive models that effectively quantify uncertainty, which is crucial for effective molecular design. One promising technique for achieving this goal is active learning through Bayesian Optimization. This will motivate the second part of my talk, where I will introduce a novel simulation-based approach for Bayesian Optimization, which has the potential to improve the efficacy of molecular design. I will analyze the convergence issues related to this approach and present empirical evidence of its effectiveness.

Roi Naveiro currently holds a Tenure Track Assistant Professor position at CUNEF Universidad. He is BSc in Physics from the University of Salamanca, MSc in Theoretical Physics and PhD in Statistics and Operations Research from the Complutense University of Madrid. His work focuses on probabilistic machine learning, Bayesian statistics and decision theory, as well as their applications to problems related to drug Discovery and materials design, among others. Naveiro has published more than 10 articles in international journals and one book. He has participated in more than five national and European research projects, and has been principal investigator in three projects with industry. In addition, he actively collaborates with AItenea Biotech, a spin-off from the Spanish National Research Council’s focused on molecular design. He has been visitor at Duke University and the Statistical and Applied Mathematical Sciences Institute (Durham, North Carolina, USA).