Dr. Walter F. de Azevedo, Jr. has been ranked among the most influential researchers in the world (Fields: Biochemistry & Molecular Biology and Biophysics) according to a database created by Journal Plos Biology (see news here). The application of the same set of metrics recognized the influence of Dr. Azevedo's work in the following years (Baas et al., 2021; Ioannidis, 2022). Regarding scientific impact (Peterson, 2005), Hirsch says that for physicists, a value for h index of 45 or higher could mean membership in the National Academy of Sciences of the USA. Dr. Azevedo is a physicist with an h-index of 50 in Scopus and 48 in the Web of Science.
Updated on May 11, 2024.
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, 1970; Hou et al., 2005) and the chemical space (Bohacek et al., 1996; Dobson, 2004; Kirkpatrick & Ellis, 2004; Lipinski & Hopkins, 2004; Shoichet, 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., 2017; Bitencourt-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 2.7.11.22) (de Ávila et al., 2017; Levin et al., 2018) and HIV-1 protease (EC 3.4.23.16) (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., 2021) to generate computational models to predict ligand-binding affinity. These programs are integrated computational tools to explore the SFS.
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