Publicaciones en las que colabora con Alfredo Vellido Alacena (11)

2024

  1. A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors

    International Journal of Molecular Sciences, Vol. 25, Núm. 12

  2. Author Correction: GPCR molecular dynamics forecasting using recurrent neural networks (Scientific Reports, (2023), 13, 1, (20995), 10.1038/s41598-023-48346-4)

    Scientific Reports

  3. Exploring Data Distributions in Machine Learning Models with SOMs

    Lecture Notes in Networks and Systems

  4. Understanding predictions of drug profiles using explainable machine learning models

    BioData Mining, Vol. 17, Núm. 1

2023

  1. GPCR molecular dynamics forecasting using recurrent neural networks

    Scientific Reports, Vol. 13, Núm. 1

  2. Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2-Adrenergic GPCR Receptor

    International Journal of Molecular Sciences, Vol. 24, Núm. 2

  3. Recognition of Conformational States of a G Protein-Coupled Receptor from Molecular Dynamic Simulations Using Sampling Techniques

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2022

  1. A Deep Learning-Based Method for Uncovering GPCR Ligand-Induced Conformational States Using Interpretability Techniques

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Long Short-Term Memory to Predict 3D Amino Acids Positions in GPCR Molecular Dynamics

    Frontiers in Artificial Intelligence and Applications

  3. Molecular Dynamics forecasting of transmembrane Regions in GPRCs by Recurrent Neural Networks

    BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings

  4. Visual Insights from the Latent Space of Generative Models for Molecular Design

    Lecture Notes in Networks and Systems