Exploring Scoring Function Space: Developing Computational Models for Drug Discovery

• Autores: Bitencourt Ferreira, Gabriela; Villarreal, Marcos A.; Quiroga, Rodrigo; Biziukova, Nadezhda; Poroikov, Vladimir; Tarasova, Olga; de Azevedo, Walter F. Jr.
• Año de publicación: 2024
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Scoring function space; Drug discovery; Machine learning; Protein space; Protein-ligand interactions; Systems biology
• Institución: Universidad Nacional de Córdoba

Impact Factor (IF) - 2023 (2024 update): 3.5 This article was made available online on 14 de junio de 2023 as a Fast Track article with title: "Exploring Scoring Function Space: Developing Computational Models for Drug Discovery".
Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre; Brazil.
Fil: Villarreal, Marcos A. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Matemática y Física; Argentina.
Fil: Villarreal, Marcos A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba; Argentina.
Fil: Quiroga, Rodrigo. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Matemática y Física; Argentina.
Fil: Quiroga, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba; Argentina.
Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow; Russia.
Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow; Russia.
Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow; Russia.
Fil: de Azevedo, Walter F. Jr. Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre; Brazil.
Fil: de Azevedo, Walter F. Jr. The Pontifical Catholic University of Rio Grande do Sul. Specialization Program in Bioinformatics, Porto Alegre; Brazil.
Background: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. Objective: Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. Methods: We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. Results: The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. Conclusion: The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.
info:eu-repo/semantics/publishedVersion
Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre; Brazil.
Fil: Villarreal, Marcos A. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Matemática y Física; Argentina.
Fil: Villarreal, Marcos A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba; Argentina.
Fil: Quiroga, Rodrigo. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Matemática y Física; Argentina.
Fil: Quiroga, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba; Argentina.
Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow; Russia.
Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow; Russia.
Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow; Russia.
Fil: de Azevedo, Walter F. Jr. Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre; Brazil.
Fil: de Azevedo, Walter F. Jr. The Pontifical Catholic University of Rio Grande do Sul. Specialization Program in Bioinformatics, Porto Alegre; Brazil.