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, vol.30, pp.809-815, 2015 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 30
  • Publication Date: 2015
  • Doi Number: 10.3109/14756366.2014.976566
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.809-815
  • Keywords: Adaptive neuro-fuzzy inference system, partial least squares regression, sequential floating forward selection, support vector regression, SELECTIVE NONPEPTIDE INHIBITORS, ISATIN SULFONAMIDE ANALOGS, BINDING AFFINITIES, POTENT, DESCRIPTORS, VALIDATION, DOCKING, DESIGN
  • Middle East Technical University Affiliated: Yes


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.