Prediction of protein-ligand binding affinities using multiple instance learning.
Accurate prediction of protein-ligand binding affinities for lead optimization in drug discovery remains an important and challenging problem on scoring functions for docking simulation. In this paper, we propose a data-driven approach that integrates multiple scoring functions to predict protein-ligand binding affinity directly. We then propose a new method called multiple instance regression based scoring (MIRS) that incorporates unbound ligand conformations using multiple scoring functions. We evaluated the predictive performance of MIRS using 100 protein-ligand complexes and their binding affinities. The experimental results showed that MIRS outperformed the 11 conventional scoring functions including LigScore, PLP, AutoDock, G-Score, D-Score, LUDI, F-Score, ChemScore, X-Score, PMF, and DrugScore. In addition, we confirmed that MIRS performed well on binding pose prediction. Our results reveal that it is indispensable to incorporate unbound ligand conformations in both binding affinity prediction and binding pose prediction. The proposed method will accelerate efficient lead optimization on structure-based drug design and provide a new direction to designing of new scoring score functions.
Advanced Technology Solutions Division, NEC Informatec Systems, Ltd., 2-6-1, Kitamigata, Takatsu-ku, Kawasaki, Kanagawa 213-8511, Japan. r-teramoto@bq.jp.nec.com
Reiji
R
Teramoto
Hisashi
H
Kashima
D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D055808:Drug Discovery; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D015394:Molecular Structure; D011485:Protein Binding; D012044:Regression Analysis; D013816:Thermodynamics
D008024:Ligands