Abstract
Molecular docking is the most commonly used technique in the structure-based approaches to in silico drug design. A key element in the algorithms for docking of bioactive molecules is the scoring function, the purpose of which is to calculate quickly and accurately the interaction energy in a protein-ligand complex and to allow for selection of appropriate ligand poses in the binding pocket. Despite the large number of comparative studies, the question of which docking programs and protocols are performing better is still unresolved and the results are often contradictory. InterCriteria analysis (ICrA), developed as a multi-criterion decision-making approach, has the capacity to assist in selecting the most appropriate scoring functions for outlining drug candidates. In this study the potential of ICrA is explored for the assessment of the performance of scoring functions available in two of the widely used software packages for docking, Molecular Operating Environment (MOE) and Genetic Optimization for Ligand Docking (GOLD). London dG and GoldScore scoring functions are in the focus of the study, being the default scoring functions in MOE and GOLD, respectively. Their performance has been examined based on the calculated binding energies and the experimental binding affinities of the benzamidine-type protease inhibitors in the protein targets of trypsin and thrombin. The obtained results have been subjected to ICrA and subsequently analyzed.
D. Jereva and T. Pencheva—Contributed equally.
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Acknowledgements
The work is supported by the National Science Fund of Bulgaria, grant DN 17/6 “A New Approach, Based on an Intercriteria Data Analysis, to Support Decision Making in in silico Studies of Complex Biomolecular Systems".
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Jereva, D., Pencheva, T., Tsakovska, I., Alov, P., Pajeva, I. (2021). Exploring Applicability of the InterCriteria Analysis to Evaluate the Performance of MOE and GOLD Scoring Functions. In: Georgiev, I., Kostadinov, H., Lilkova, E. (eds) Advanced Computing in Industrial Mathematics. BGSIAM 2018. Studies in Computational Intelligence, vol 961. Springer, Cham. https://doi.org/10.1007/978-3-030-71616-5_18
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