6 papers
Sesame: Opening the door to protein pockets
Raúl Miñán, Carles Perez-Lopez, Javier Iglesias +2
Molecular docking is a cornerstone of drug discovery, relying on high-resolution ligand-bound structures to achieve accurate predictions. However, obtaining these structures is oft…
sHGCN: Simplified hyperbolic graph convolutional neural networks
Pol Arévalo, Alexis Molina, Ãlvaro Ciudad
Hyperbolic geometry has emerged as a powerful tool for modeling complex, structured data, particularly where hierarchical or tree-like relationships are present. By enabling embedd…
Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models
Adrián Morales-Pastor, Raquel Vázquez-Reza, MiÅosz Wieczór +5
RNA is a vital biomolecule with numerous roles and functions within cells, and interest in targeting it for therapeutic purposes has grown significantly in recent years. However, f…
Are Protein Language Models Compute Optimal?
Yaiza Serrano, Ãlvaro Ciudad, Alexis Molina
While protein language models (pLMs) have transformed biological research, the scaling laws governing their improvement remain underexplored. By adapting methodologies from NLP sca…
Scoreformer: A Surrogate Model For Large-Scale Prediction of Docking Scores
Ãlvaro Ciudad, Adrián Morales-Pastor, Laura Malo +3
In this study, we present ScoreFormer, a novel graph transformer model designed to accurately predict molecular docking scores, thereby optimizing high-throughput virtual screening…
GeoDirDock: Guiding Docking Along Geodesic Paths
Raúl Miñán, Javier Gallardo, Ãlvaro Ciudad +1
This work introduces GeoDirDock (GDD), a novel approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions. GDD guides the deno…