2 citations · 3 across the 6 of their papers we have counts for
7 papers · 1 filter
Two-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition
Violeta Basten-Romero, Rubén Muñoz-Tafalla, Anna María Díaz-Rovira +3
Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remains largely unexplored. We study…
Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing
Bertran Miquel-Oliver, Manel Gil-Sorribes, Victor Guallar +1
Graph Neural Networks struggle to capture long-range dependencies due to over-squashing, where information from exponentially growing neighborhoods must pass through a small number…
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…
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation
Júlia Vilalta-Mor, Alexis Molina, Laura Ortega Varga +2
Simultaneously optimizing molecules against multiple therapeutic targets remains a profound challenge in drug discovery, particularly due to sparse rewards and conflicting design c…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
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…