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q-bio.BM2024
Explainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions
Francesco Ferri, Marco Cannariato, Lorenzo Pallante +2
This work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting fro…
q-bio.BM2024★ 3 cited
LightCPPgen: An Explainable Machine Learning Pipeline for Rational Design of Cell Penetrating Peptides
Gabriele Maroni, Filip Stojceski, Lorenzo Pallante +3
Cell-penetrating peptides (CPPs) are powerful vectors for the intracellular delivery of a diverse array of therapeutic molecules. Despite their potential, the rational design of CP…