Exploring Scoring Function Space: Developing Computational Models for Drug Discovery
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".
| Main Authors: | , , , , , , |
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| Format: | info:eu-repo/semantics/publishedVersion |
| Language: | eng |
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2024
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| Online Access: | http://hdl.handle.net/11086/552745 https://www.ingentaconnect.com/content/ben/cmc/2024/00000031/00000017/art00005 https://pubmed.ncbi.nlm.nih.gov/36944627/ http://doi.org/10.2174/0929867330666230321103731 |
| _version_ | 1806282346109861888 |
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| author | Bitencourt Ferreira, Gabriela Villarreal, Marcos A. Quiroga, Rodrigo Biziukova, Nadezhda Poroikov, Vladimir Tarasova, Olga de Azevedo, Walter F. Jr. |
| author2 | https://orcid.org/0000-0002-3120-8256 |
| author_facet | https://orcid.org/0000-0002-3120-8256 Bitencourt Ferreira, Gabriela Villarreal, Marcos A. Quiroga, Rodrigo Biziukova, Nadezhda Poroikov, Vladimir Tarasova, Olga de Azevedo, Walter F. Jr. |
| author_sort | Bitencourt Ferreira, Gabriela |
| collection | Repositorio Digital Universitario |
| description | 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". |
| format | info:eu-repo/semantics/publishedVersion |
| id | rdu-unc.552745 |
| institution | Universidad Nacional de Cordoba |
| language | eng |
| publishDate | 2024 |
| record_format | dspace |
| spelling | rdu-unc.5527452024-07-31T12:25:45Z Exploring Scoring Function Space: Developing Computational Models for Drug Discovery Bitencourt Ferreira, Gabriela Villarreal, Marcos A. Quiroga, Rodrigo Biziukova, Nadezhda Poroikov, Vladimir Tarasova, Olga de Azevedo, Walter F. Jr. https://orcid.org/0000-0002-3120-8256 https://orcid.org/0000-0001-8223-5193 https://orcid.org/0000-0001-5015-0531 https://orcid.org/0000-0002-2044-1327 https://orcid.org/0000-0001-7937-2621 https://orcid.org/0000-0002-3723-7832 https://orcid.org/0000-0001-8640-357X Scoring function space Drug discovery Machine learning Protein space Protein-ligand interactions Systems biology 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". 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. 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. 2024-07-15T23:59:08Z 2024-07-15T23:59:08Z 2024-05-01 article Bitencourt-Ferreira, G., Villarreal, M. A., Quiroga, R., Biziukova, N., Poroikov, V., Tarasova, O., & de Azevedo Junior, W. F. (2024). Exploring Scoring Function Space: Developing Computational Models for Drug Discovery. Current Medicinal Chemistry, 31(17), 2361-2377. http://hdl.handle.net/11086/552745 1875-533X https://www.ingentaconnect.com/content/ben/cmc/2024/00000031/00000017/art00005 https://pubmed.ncbi.nlm.nih.gov/36944627/ http://doi.org/10.2174/0929867330666230321103731 eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| spellingShingle | Scoring function space Drug discovery Machine learning Protein space Protein-ligand interactions Systems biology Bitencourt Ferreira, Gabriela Villarreal, Marcos A. Quiroga, Rodrigo Biziukova, Nadezhda Poroikov, Vladimir Tarasova, Olga de Azevedo, Walter F. Jr. Exploring Scoring Function Space: Developing Computational Models for Drug Discovery |
| title | Exploring Scoring Function Space: Developing Computational Models for Drug Discovery |
| title_full | Exploring Scoring Function Space: Developing Computational Models for Drug Discovery |
| title_fullStr | Exploring Scoring Function Space: Developing Computational Models for Drug Discovery |
| title_full_unstemmed | Exploring Scoring Function Space: Developing Computational Models for Drug Discovery |
| title_short | Exploring Scoring Function Space: Developing Computational Models for Drug Discovery |
| title_sort | exploring scoring function space developing computational models for drug discovery |
| topic | Scoring function space Drug discovery Machine learning Protein space Protein-ligand interactions Systems biology |
| url | http://hdl.handle.net/11086/552745 https://www.ingentaconnect.com/content/ben/cmc/2024/00000031/00000017/art00005 https://pubmed.ncbi.nlm.nih.gov/36944627/ http://doi.org/10.2174/0929867330666230321103731 |
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