Alex G. C. de Sá

This paper introduces DDMuffin, a deep learning framework for predicting protein-ligand binding affinities and mutation-induced changes, showing strong performance on benchmark datasets and offering insights for drug design.

PRIMITI predicts miRNA-target mRNA interactions using machine learning with CLIP-seq and expression data, achieving high AUC scores and outperforming existing methods.

AI-m6ARS predicts human m6A methylation sites using sequence, conservation, and geographical features, achieving high accuracy and offering a user-friendly web server.

This study evaluates pathogenicity predictors for GPCR mutations using ClinVar and MutHTP datasets, finding reliable performance on ClinVar but poor performance on MutHTP, and identifies key mutation characteristics linked to disease.

The paper shows that using structure-based predictors of intolerance improves the identification of pathogenic variants in whole exome sequences of patients with developmental and epileptic encephalopathies.