Learning from the ligand: using ligand-based features to improve binding affinity prediction.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Feb 1, 2020
Abstract
MOTIVATION: Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand complex, with limited information about the chemical or topological properties of the ligand itself.