Uploaded July 2026 | Updated September 2026, 2 weeks ago
Title: AI and Structural Biology: From Protein Structure to Molecular Design
Speaker: Alberto Perez, University of Florida
Abstract: Proteins are the molecular machines of life. They digest food, transport oxygen, and regulate nearly every biological process. When these systems malfunction, disease emerges. Understanding how proteins work requires knowing not just their structures, but how they move and interact—often in highly dynamic and flexible ways.
Recent advances in artificial intelligence, highlighted by the impact of AlphaFold and recognized by the Nobel Prize in Chemistry, have transformed our ability to predict protein structures with remarkable accuracy. This has fundamentally changed structural biology, opening the door to large-scale modeling and design.
In this talk, I will trace the transition from AI systems that mastered complex games such as Go to those that can predict the structure of proteins. I will highlight how community-wide efforts—through international competitions and shared databases—enabled these breakthroughs. Finally, I will discuss how combining AI with physics-based approaches, such as molecular simulations, is helping us move beyond structure prediction to uncover the principles that govern biological function.
Profile: Alberto Perez is an Associate Professor of Chemistry and Associate Director of the Quantum Theory Project at the University of Florida. His research focuses on developing integrative computational approaches that combine artificial intelligence and molecular simulations to understand and design biomolecular interactions.
He completed his Ph.D. in Barcelona and carried out postdoctoral training at University of California, San Francisco and Stony Brook University with Ken A. Dill. His group develops methods to model flexible systems, including protein–protein and protein–peptide interactions, with applications in peptide-based drug discovery and molecular design..
Perez Bio - Chemistry: https://chem.ufl.edu/people-2/faculty-2/perez-bio/
Find out more about the TSVP on the program website: oist.jp/visiting-program
#AI #AlphaFold #StructuralBiology #Biology #Protein #MolecularDesign #AIwithPhysics-basedApproaches #OIST #TSVP #Theoretical #Science #VisitingProgram #Okinawa #research
Title: AI and Structural Biology: From Protein Structure to Molecular Design
Speaker: Alberto Perez, University of Florida
Abstract: Proteins are the molecular machines of life. They digest food, transport oxygen, and regulate nearly every biological process. When these systems malfunction, disease emerges. Understanding how proteins work requires knowing not just their structures, but how they move and interact—often in highly dynamic and flexible ways.
Recent advances in artificial intelligence, highlighted by the impact of AlphaFold and recognized by the Nobel Prize in Chemistry, have transformed our ability to predict protein structures with remarkable accuracy. This has fundamentally changed structural biology, opening the door to large-scale modeling and design.
In this talk, I will trace the transition from AI systems that mastered complex games such as Go to those that can predict the structure of proteins. I will highlight how community-wide efforts—through international competitions and shared databases—enabled these breakthroughs. Finally, I will discuss how combining AI with physics-based approaches, such as molecular simulations, is helping us move beyond structure prediction to uncover the principles that govern biological function.
Profile: Alberto Perez is an Associate Professor of Chemistry and Associate Director of the Quantum Theory Project at the University of Florida. His research focuses on developing integrative computational approaches that combine artificial intelligence and molecular simulations to understand and design biomolecular interactions.
He completed his Ph.D. in Barcelona and carried out postdoctoral training at University of California, San Francisco and Stony Brook University with Ken A. Dill. His group develops methods to model flexible systems, including protein–protein and protein–peptide interactions, with applications in peptide-based drug discovery and molecular design..
Perez Bio - Chemistry: https://chem.ufl.edu/people-2/faculty-2/perez-bio/
Find out more about the TSVP on the program website: oist.jp/visiting-program
#AI #AlphaFold #StructuralBiology #Biology #Protein #MolecularDesign #AIwithPhysics-basedApproaches #OIST #TSVP #Theoretical #Science #VisitingProgram #Okinawa #research








![Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)
[Title]
Florian Yger - A Geometric Adventure in Machine Learning: Learning with Invariances, Structures, and Prior Knowledge (TSVP Talk at OIST)
[Description]
Florian Yger is visiting OIST from 2024-05-20 until 2024-08-15 through the Theoretical Sciences Visiting Program (TSVP). Find out more about the TSVP on the program website:
https://www.oist.jp/visiting-program.
Abstract: In this presentation, we embark on a journey through the landscapes of machine learning, focusing on the role of representation learning. Traditional approaches often restrict themselves to Euclidean spaces, yet many real-world data, such as graphs and covariances, defy this simplistic framework. We explore how embracing non-Euclidean geometries—specifically curved spaces like Riemannian manifolds—unlocks new possibilities in understanding and predicting structured data. This framework allows for the incorporation of prior knowledge such as constraints and invariances into machine learning algorithms.
Central to our exploration is the Fréchet averaging problem, a fundamental tool that generalizes the well-know average to metric spaces. From this cornerstone, we derive many extensions ranging from graph averaging to dimensionality reduction on Riemannian manifolds. We illustrate their practical implications through numerical experiments on biomedical data.
Motivated by challenging applications, this presentation not only highlights the limitations of Euclidean-centric approaches but also underscores the potential of geometry-aware representation learning. Join us as we navigate this geometric adventure into the curved realm of geometry-aware representation learning.
Profile: Florian Yger is an associate professor at Université Paris-Dauphine since 2015, teaching Data Analysis and Machine Learning in the department MIDO. Within the LAMSADE, he is part of the team MILES which focuses on trustworthy Machine Learning and Explainable AI. From 2014 to 2015, he was a JSPS postdoctoral fellow in the laboratory of Prof. Sugiyama at Tokyo University. He received his PhD in Computer science from LITIS, Université de Rouen under the supervision of Alain Rakotomamonjy in 2013. He contributes to the problem of representation learning with a particular interest in the representation of structured data (graphs, covariance matrices,…) and the development of learning algorithm for non-Euclidean spaces. This work has many applications ranging from signal processing (EEG signals and Brain Computer Interface) and to image processing (Paintings for art style recognition). More recently, within MILES teams, he addresses the questions of trust, explainability and interpretability in machine learning models with a focus counterfactual reasoning on data. He recently started studying the interplay between computational social choice and machine learning in the context of voter’s opinion aggregation. He is a visiting researcher at RIKEN AIP, Japan since 2017, is a member of the Prairie (PaRis Artificial Intelligence Research InstitutE) where he holds a junior chair, and an Affiliate of the Theoretical Sciences Visiting Program (TSVP) at OIST.
#OIST #OIST_TSVP #MachineLearning #Geometry #Manifolds #Theoretical #Science #VisitingProgram #Okinawa #TSVP Florian Yger: A Geometric Adventure in Machine Learning (TSVP Talk at OIST)](https://i.ytimg.com/vi/KlU1mS1xr3I/mqdefault.jpg)

