Rubén Sánchez-García

This paper introduces a scalable method for searching 3D protein structure similarity across entire proteomes using deep learning and vector databases.

The paper introduces cryoPARES, a deep learning method for efficient cryo-EM image alignment, enabling faster and automated structure determination with reduced manual intervention.

This paper introduces a graph database approach to expand fragment merging by incorporating bioisosteric substructures, improving computational efficiency and chemical diversity in drug design.

The paper identifies a limitation in existing cryo-EM map anisotropy measurement methods and shows that post-processing can reduce anisotropy.

This study validates a machine learning platform, NAP-CNB, for identifying tumor neoantigens using RNAseq data, showing improved antitumor responses with multiantigen vaccination.

The paper uses a graph database to identify fragment merges from chemical catalogues, finding complementary results to traditional similarity searches.

DeepEMhancer improves cryo-EM map quality using deep learning, achieving cleaner and more detailed maps on test cases like SARS-CoV-2 RNA polymerase.

NAP-CNB is a web server tool that predicts MHC-I-restricted T cell epitopes in mice using recurrent neural networks, achieving an AUC of 0.95 for H-2 peptide ligands.

MicrographCleaner uses deep learning to improve cryo-EM micrograph preprocessing by identifying suitable regions for particle picking.