P. Agrawal

Synthetic data for more accurate deep learning models in molecular science: a test case of protein-ligand binding affinity prediction

This study shows that incorporating synthetic molecular dynamics data improves deep learning models for protein–ligand binding affinity prediction beyond static experimental structures, and highlights that dynamic synthetic datasets can enable deep learning models to outperform conventional methods such as MM-PBSA while remaining computationally efficient.