Compressed images for affinity prediction (CIFAP): a study on predicting binding affinities for checkpoint kinase 1 protein inhibitors

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

JOURNAL OF CHEMOMETRICS, vol.27, pp.155-164, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27
  • Publication Date: 2013
  • Doi Number: 10.1002/cem.2503
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.155-164
  • Middle East Technical University Affiliated: Yes


Analyses of known protein-ligand interactions play an important role in designing novel and efficient drugs, contributing to drug discovery and development. Recently, machine learning methods have proven useful in the design of novel drugs, which utilize intelligent techniques to predict the outcome of unknown protein-ligand interactions by learning from the physical and geometrical properties of known protein-ligand interactions. The aim of this study is to work through a specific example of a novel computational method, namely compressed images for affinity prediction (CIFAP), in which binding affinities for structurally related ligands in complexes with human checkpoint kinase 1 (CHK1) are predicted. The CIFAP algorithm presented here relates published pIC 50 values of 57 compounds, derived from a thienopyridine pharmacophore, in complexes with CHK1 to their two-dimensional (2D) electrostatic potential images compressed in orthogonal dimensions. Patterns obtained from the 2D images are then used as inputs in regression and learning algorithms such as support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) methods to validate the experimental pIC 50 values. This study revealed that the 2D image pixels in the vicinity of bound ligand surfaces provide more relevant information to make correlations with the empirical pIC 50 values. As compared with ANFIS, SVR gave rise to the lowest root mean square errors and the greatest correlations, suggesting that SVR could be a plausible choice of machine learning methods in predicting binding affinities by CIFAP. Copyright (c) 2013 John Wiley & Sons, Ltd.