Exploring Scoring Function Space: Developing Computational Models for Drug Discovery
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Date
2024-05-01Author
Bitencourt Ferreira, Gabriela
Villarreal, Marcos A.
Quiroga, Rodrigo
Biziukova, Nadezhda
Poroikov, Vladimir
Tarasova, Olga
de Azevedo, Walter F. Jr.
ORCID
https://orcid.org/0000-0002-3120-8256https://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
Metadata
Show full item recordAbstract
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.
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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.
Other links
https://www.ingentaconnect.com/content/ben/cmc/2024/00000031/00000017/art00005https://pubmed.ncbi.nlm.nih.gov/36944627/
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