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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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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Citation Formats

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.