9 citations · 9 across the 1 of their papers we have counts for
5 papers
Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models
Adrián Morales-Pastor, Raquel Vázquez-Reza, Miłosz 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, Álvaro 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
Álvaro 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, Álvaro 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…
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks
Isaac Filella-Merce, Alexis Molina, Marek Orzechowski +7
Traditional drug discovery programs are being transformed by the advent of machine learning methods. Among these, Generative AI methods (GM) have gained attention due to their abil…