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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Erdas O., Andac C. A. , Gurkan-Alp A. S. , Alpaslan F. N. , Buyukbingol E.

JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY, cilt.30, ss.809-815, 2015 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30
  • Basım Tarihi: 2015
  • Doi Numarası: 10.3109/14756366.2014.976566
  • Sayfa Sayıları: ss.809-815


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.