Abstract
In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the chemical space is newer, later 1990s. With the progress of computational methodologies to create machine-learning models to predict the ligand-binding affinity, clearly there is a need for novel approaches to the problem of protein-ligand interactions. New abstractions are required to guide the conceptual analysis of the molecular recognition problem. Using a systems approach, we proposed to address protein-ligand scoring functions using the modern idea of the scoring function space. In this chapter, we describe the fundamental concept behind the scoring function space and how it has been applied to develop the new generation of targeted-scoring functions.
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Acknowledgments
This work was supported by grants from CNPq (Brazil) (308883/2014-4). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001. GB-F acknowledges support from PUCRS/BPA fellowship. WFA is a senior researcher for CNPq (Brazil) (Process Numbers: 308883/2014-4 and 309029/2018-0).
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Bitencourt-Ferreira, G., de Azevedo, W.F. (2019). Exploring the Scoring Function Space. In: de Azevedo Jr., W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, vol 2053. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9752-7_17
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DOI: https://doi.org/10.1007/978-1-4939-9752-7_17
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