9 citations · 10 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
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…