4 papers
Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
Manel Gil-Sorribes, Júlia Vilalta-Mor, Isaac Filella-Mercè +4
Accurate drug-target interaction (DTI) prediction is essential for computational drug discovery, yet existing models often rely on single-modality predefined molecular descriptors…
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…