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Ligand-protein docking is an optimization problem based
on predicting the position of a ligand with the lowest binding energy
in the active site of the receptor. Molecular docking problems are traditionally
tackled with single-objective, as well as with multi-objective
approaches, to minimize the binding energy. In this paper, we propose a
novel multi-objective formulation that considers: the Root Mean Square
Deviation (RMSD) di erence in the coordinates of ligands and the binding
(intermolecular) energy, as two objectives to evaluate the quality of
the ligand-protein interactions. To determine the kind of Pareto front
approximations that can be obtained, we have selected a set of representative
multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and
MOEA/D. Their performances have been assessed by applying two main
quality indicators intended to measure convergence and diversity of the
fronts. In addition, a comparison with LGA, a reference single-objective
evolutionary algorithm for molecular docking (AutoDock) is carried out.
In general, SMPSO shows the best overall results in terms of energy and
and RMSD (value lower than 2 A for successful docking results). This new
multi-objective approach shows an improvement over the ligand-protein
docking predictions that could be promising in in silico docking studies
to select new anticancer compounds for therapeutic targets that are
multidrug resistant.Ministerio de Ciencia e Innovación TIN2011-25840Junta de Andalucía P11-TIC-7529Junta de Andalucía P12-TIC-1519European Cooperation in Science and Technology CA1514
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