Greetings! Thank you very much for dropping by. It follows a brief description of my research goals and modest accomplishments.
My scientific interests are interdisciplinary, with three main emphases: artificial intelligence, complex systems, and computational systems biology. In my studies, I developed several free software to explore the concept of Scoring Function Space. As a result of my research, I published over 200 scientific publications about protein structures, computer models of complex systems, and simulations of protein systems. These publications generated over 8,000 citations and an h-index of 50 in Scopus.
Due to the impact of my work, I have been ranked among the most influential researchers in the world (Fields: Biophysics, Biochemistry & Molecular Biology, and Biomedical Research) according to a database created by Journal Plos Biology (see news here). The application of the same set of metrics recognized the influence of my work in the following years (Baas et al., 2021; Ioannidis, 2022; Ioannidis, 2023; Ioannidis, 2024). Not bad for a poor dreamer who was a shoe seller at Zapata stores in São Paulo and had the gold opportunity to study at the University of São Paulo with a scholarship for food and housing. I was 23 when I initiated my undergraduate studies and the first in my family to have access to higher education.
Regarding scientific impact (Peterson, 2005), Hirsch says that for a physicist, a value for the h index of 45 or higher could mean membership in the National Academy of Sciences of the USA. So far, no invitations...
I will continue influencing and impacting science with low-budget and interdisciplinary projects, challenging denialism with science and technology. The fight against denialism is a continuing work, and scientists should not forget their role in a complex society where social media gave the right to speak to legions of imbeciles.
“Social media gives the right to speak to legions of imbeciles who previously only spoke at the bar after a glass of wine, without damaging the community. They were immediately silenced, but now they have the same right to speak as a Nobel Prize winner. It’s the invasion of imbeciles.”
Umberto Eco. Source: Quote Investigator
"Let the light of science end the darkness of denialism." My quote (DOI: https://doi.org/10.2174/092986732838211207154549).
Elsevier Data Repository: 2021 data-update 2022 data-update 2023 data-update 2024 data-update
News
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 (Bitencourt-Ferreira et al., 2024).
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 artificial intelligence 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; Bitencourt-Ferreira et al., 2024; de Azevedo Jr. et al., 2024) 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; Bitencourt-Ferreira et al., 2024; de Azevedo Jr. et al., 2024).
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., 2024; de Azevedo Jr. et al., 2024). By the use of supervised machine learning techniques, we can explore this SFS (Bitencourt-Ferreira & de Azevedo Jr., 2019; da Silva et al., 2020; de Azevedo Jr. et al., 2024) 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; da Silva et al., 2020; de Azevedo Jr. et al., 2024) 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; de Azevedo Jr. et al., 2024), 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 (Bitencourt-Ferreira et al., 2024).