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Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design
Date
2019-11-01
Author
Erdas-Cicek, Ozlem
Atac, Ali Osman
Gurkan-Alp, A. Selen
Buyukbingol, Erdem
Alpaslan, Ferda Nur
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Understanding the interaction between drug molecules and proteins is one of the main challenges in drug design. Several tools have been developed recently to decrease the complexity of the process. Artificial intelligence and machine learning methods offer promising results in predicting the binding affinities. It becomes possible to do accurate predictions by using the known protein-ligand interactions. In this study, the electrostatic potential values extracted from 3-dimensional grid cubes of the drug-protein binding sites are used for predicting binding affinities of related complexes. A new algorithm with a dynamic feature selection method was implemented, which is derived from Compressed Images For Affinity Prediction (CIFAP) study, to predict binding affinities of Checkpoint Kinase 1 and Caspase 3 inhibitors.
Subject Keywords
General Chemistry
,
General Chemical Engineering
,
Library and Information Sciences
,
Computer Science Applications
URI
https://hdl.handle.net/11511/36320
Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
DOI
https://doi.org/10.1021/acs.jcim.9b00206
Collections
Department of Computer Engineering, Article
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BibTeX
O. Erdas-Cicek, A. O. Atac, A. S. Gurkan-Alp, E. Buyukbingol, and F. N. Alpaslan, “Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design,”
JOURNAL OF CHEMICAL INFORMATION AND MODELING
, pp. 4654–4662, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36320.