Sesame: Opening the door to protein pockets

Raúl Miñán, Carles Perez-Lopez, Javier Iglesias-Fernández, Alvaro Ciudad Serrano, Alexis Molina

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: Yes

Keywords: flow matching, generation of holo conformations, opening cryptic cavities

TL;DR: We train a generative flow matching model on protein apo-holo conformations to generate open cavities with competitive performance

Abstract: Molecular docking is a cornerstone of drug discovery, relying on high-resolution ligand-bound structures to achieve accurate predictions. However, obtaining these structures is often costly and time-intensive, limiting their availability. In contrast, ligand-free structures are more accessible but suffer from reduced docking performance due to pocket geometries being less suited for ligand accommodation in apo structures. Traditional methods for artificially inducing these conformations, such as molecular dynamics simulations, are computationally expensive. In this work, we introduce Sesame, a generative model designed to predict this conformational change efficiently. By generating geometries better suited for ligand accommodation at a fraction of the computational cost, Sesame aims to provide a scalable solution for improving virtual screening workflows.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Raúl_Miñán1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 96