Current Drug Targets

Frontiers Section Editor (Bioinformatics and Biophysics) for the Current Drug Targets ISSN: 1873-5592

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Current Medicinal Chemistry

Section Editor (Bioinformatics in Drug Design and Discovery) for the Current Medicinal Chemistry ISSN: 1875-533X

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Journal of Molecular Structure

Member of the Editorial Board of the Journal of Molecular Structure ISSN: 0022-2860

Journal of Molecular Structure Link

Molecular Diversity

Member of the Editorial Board of Molecular Diversity ISSN: 1381-1991 (Print) 1573-501X (Online)

Molecular Diversity Link

Exploration of Drug Science

Associate Editor for Exploration of Drug Science

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Frontiers in Chemistry

Reviewer Editor for Frontiers in Chemistry ISSN: 2296-2646

Frontiers in Chemistry Link

Organic and Medicinal Chemistry International Journal

Member of the Editorial Board for the Organic and Medicinal Chemistry International Journal ISSN: 2474-7610

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Aula Magna [>>]  

Exploring the Scoring Function Space [>>]

     A view of the scoring function space as a way to develop a computational model to predict ligand-binding affinity. 

We envisage protein-ligand interaction as a result of the relation between the protein space (Smith, 1970Hou et al., 2005and the chemical space (Bohacek et al., 1996Dobson, 2004Kirkpatrick & Ellis, 2004Lipinski & Hopkins, 2004Shoichet, 2004; Stockwell, 2004), and we propose to approach these sets as a complex system, where the application of computational methodologies could contribute to understanding the structural basis for the specificity of ligands for proteins. Such approaches can create novel scoring functions to predict binding affinity with superior predictive power compared with classical scoring functions (also known as universal scoring functions)(Ross et al., 2013) available in docking programs (de Azevedo Jr., 2021). We propose to use the abstraction of a mathematical space composed of infinite computational models to predict ligand-binding affinity, named scoring function space (SFS) (Ross et al., 2013; Heck et al., 2017Bitencourt-Ferreira & de Azevedo Jr., 2019; Veríssimo et al., 2022). With the development of the SFS concept, we expect to merge the holistic view of systems biology with machine-learning methods to contribute to drug discovery projects (Bitencourt-Ferreira et al., 2023). By the use of supervised machine learning techniques, we can explore this SFS (Bitencourt-Ferreira & de Azevedo Jr., 2019; Bitencourt-Ferreira et al., 2023) to build a computational model targeted to a specific protein system (targeted-scoring function) (Seifert, 2009). For instance, we created targeted-scoring functions for cyclin-dependent kinases (EC (de Ávila et al., 2017; Levin et al., 2018) and HIV-1 protease (EC (Pintro & de Azevedo, 2017). We developed the programs SAnDReS (Xavier et al., 2016; Bitencourt-Ferreira & de Azevedo Jr., 2019), SFSXplorer, and Taba (da Silva et al., 2020; Bitencourt-Ferreira et al., 2021to generate computational models to predict ligand-binding affinity. These programs are integrated computational tools to explore the SFS.