Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design

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...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2019-11, Vol.59 (11), p.4654-4662
Main Authors: Erdas-Cicek, Ozlem, Atac, Ali Osman, Gurkan-Alp, A. Selen, Buyukbingol, Erdem, Alpaslan, Ferda Nur
Format: Article
Language:English
Subjects:
Affinity
Algorithms
Artificial intelligence
Binding sites
Cubes
Dimensional analysis
Kinases
Machine learning
Mathematical analysis
Predictions
Proteins
Three dimensional analysis
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Summary: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.
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.9b00206