In our research, we see protein-ligand interaction as a result of the relation between the protein space (Smith, 1970) and the chemical space (Bohacek et al., 1996; Dobson, 2004; Kirkpatrick & Ellis, 2004; Lipinski & Hopkins, 2004; Shoichet, 2004; Stockwell, 2004), and we propose to represent these sets as a unique complex system, where the application of computational methodologies may contribute to generate models to predict protein-ligand binding affinities. Such approaches have the potential to create novel semi-empirical force fields to predict binding affinity with superior predictive power when compared with standard methodologies. SFSXplorer is an acronym for Scoring Function Space eXplorer. This computational tool explores the scoring function space with a hybrid algorithm, where we vary energy terms and adjust their relative weights using machine learning algorithms. We propose to use the abstraction of a mathematical space composed of infinite computational models to predict ligand-binding affinity. We named this space as the scoring function space (Heck et al., 2017; Bitencourt-Ferreira & de Azevedo Jr., 2019). By the use of supervised machine learning techniques is possible to explore this scoring function space and build a computational model targeted to a specific biological system.
References
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da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73.
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Keywords: Computational systems biology; systems biology; systems approach; machine learning; protein; ligand; interactions; protein-ligand interactions; drug design; drug discovery; binding affinity.