Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein-ligand interactions based on a study on caspase 3 inhibitors

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Date

2015-01-01

Author

Erdas, Ozlem
Andac, Cenk. A.
Gurkan-Alp, A. Selen
Alpaslan, Ferda Nur
Buyukbingol, Erdem

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The aim of this study is to propose an improved computational methodology, which is called Compressed Images for Affinity Prediction-2 (CIFAP-2) to predict binding affinities of structurally related protein-ligand complexes. CIFAP-2 method is established based on a protein-ligand model from which computational affinity information is obtained by utilizing 2D electrostatic potential images determined for the binding site of protein-ligand complexes. The quality of the prediction of the CIFAP-2 algorithm was tested using partial least squares regression (PLSR) as well as support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which are highly promising prediction methods in drug design. CIFAP-2 was applied on a protein-ligand complex system involving Caspase 3 (CASP3) and its 35 inhibitors possessing a common isatin sulfonamide pharmacophore. As a result, PLSR affinity prediction for the CASP3-ligand complexes gave rise to the most consistent information with reported empirical binding affinities (pIC50) of the CASP3 inhibitors.

Subject Keywords

Pharmacology, Drug Discovery, General Medicine

URI

https://hdl.handle.net/11511/40512

Journal

JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY

DOI

https://doi.org/10.3109/14756366.2014.976566

Collections

Department of Computer Engineering, Article

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Citation Formats

O. Erdas, C. A. Andac, A. S. Gurkan-Alp, F. N. Alpaslan, and E. Buyukbingol, “Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein-ligand interactions based on a study on caspase 3 inhibitors,” JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY, pp. 809–815, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40512.