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Finding the orientation of a ligand (small molecule) with the lowest binding energy to the macromolecule (receptor) is a complex optimization problem, commonly called ligand–protein docking. This problem has been usually approached by minimizing a single objective that corresponds to the final free energy of binding. In this work, we propose a new multi-objective strategy focused on minimizing: (1) the root mean square deviation (RMSD) between the co-crystallized and predicted ligand atomic coordinates, and (2) the ligand–receptor intermolecular energy. This multi-objective strategy provides the molecular biologists with a range of solutions computing different RMSD scores and intermolecular energies. A set of representative multi-objective algorithms, namely NSGA-II, SMPSO, GDE3 and MOEA/D, have been evaluated in the scope of an extensive set of docking problems, which are featured by including HIV-proteases with flexible ARG8 side chains and their inhibitors. As use cases for biological validation, we have included a set of instances based on new retroviral inhibitors to HIV-proteases. The proposed multi-objective approach shows that the predictions of ligand’s pose can be promising in cases in which studies in silico are necessary to test new candidate drugs (or analogue drugs) to a given therapeutic target.

Optimizing ligand conformations in flexible protein targets: a multi-objective strategy