Novel knowledge-based potentials for ligand docking: variational approach to the old problem.
The variational approach of evaluation for knowledge-based potentials is considered for the first time. In this approach, the problem to derive knowledge-based potentials is solved as the optimization task in the multiparametric model of atom types, reference states and interaction cutoff radii. Using analogy to liquid state theory we offered four new reference states and derived corresponding knowledge-based potentials. The cutoff radii and atom types are optimized to minimize averaged root-mean square deviations (RMSD) of the ligand docked positions regarding to the experimentally determined poses. The number of atom types is varied on the developed atom type tree with 6 root (C, N, O, S, P and the halogen type) and 49 apical atom types. We showed a pronounced effect of atom type choice on docking accuracy and proved that splitting of elements C, N and O of the periodic system up to the 18 optimal atom types essentially improves docking accuracy.
Ruvinsky
A M
AM
Force Field Laboratory, Algodign, LLC, B. Sadovaya, 8, 103379, Moscow, Russia. anatoly.ruvinsky@algodign.com
Kozintsev
A V
AV
D008024:Ligands; D008956:Models, Chemical; D015394:Molecular Structure; D010100:Oxygen / Q000737:chemistry
D008024:Ligands; D010100:Oxygen