Abstract
BackgroundDevelopment of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design.ResultsIn this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected.ConclusionThe multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.
Highlights
Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research
Because of the different preferences in the selection of pareto-optimal solutions with ε-MOEA, the results of three different approaches will be compared with individual scoring functions, namely one with preferred energy score for εMOEA (MOEA_Nrg), the other with preferred contact score for ε-MOEA (MOEA_Cnt), and the third for evaluation function multi-objective optimization genetic algorithm (EFMOGA)
Development of a fast and accurate scoring function in virtual screening is still a hot and pending issue in current research, different scoring functions focus on different aspects of ligand binding, and no single scoring can satisfy all the systems well, consensus score was put forward.[35, 36]
Summary
Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. An attractive goal is to develop computer programs capable of automatically evaluating large-scale chemical libraries (databases). These computational methods are referred to as virtual screening (VS)[4]. As the fitness during the optimization process, scoring function should be fast and accurate enough, allowing simultaneous ranking of the retrieved poses in the optimization process. Based on this idea, several scoring functions have been developed [31,32,33]. Heuristic docking and consensus score strategies are often used in virtual screening [34,35,36]
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