CIFAP is implemented in two phases: In the first phase, CIFAP involves preperation of orthogonal 2D-compressed grid images of experimentally determined or docked coordinates of ligand–receptor complexes. ![]() We previously reported Citation12 a novel pharmacophore-based drug development algorithm for data representation, namely Compressed Images for Binding Affnity Prediction (CIFAP), to predict binding affinities for an array of different ligands (carrying a common pharmacophore) interacting with the binding site of a protein. It is possible to search for or predict specific properties of new drugs by using information from known ligand–receptor interactions in association with bioinformatics and machine learning methods Citation11. Intelligent computational methods have recently become popular in drug design Citation2–10. In other words, docking energy functions mostly employ electrostatic and Van der Waals energy terms determined in space, but not in aqueous media. However, docking energy functions provided by docking programs are not always reliable as majority of docking energy functions do not include electrostatic and non-electrostatic contributions to solvation. Computational methods such as docking and molecular dynamics have become powerful, time-saving and cheaper methods for providing detailed information on protein–ligand interactions. ![]() Understanding protein–ligand interactions at a molecular level is important to design new drugs which are safe and efficient. Experimental drug discovery and development is a time-consuming and expensive process which may involve testing a large library of compounds that frustratingly yield many failures in drug discovery. Increasing stored knowledge of drug actions at a molecular level renders development of some novel drugs which are safer and more efficient in medical treatments Citation1. Significant progress has been made by scientists towards understanding diseases at molecular levels by developing new methods in the field of genomics, proteomics as well as medicine. As a result, PLSR affinity prediction for the CASP3–ligand complexes gave rise to the most consistent information with reported empirical binding affinities (pIC 50) of the CASP3 inhibitors. CIFAP-2 was applied on a protein–ligand complex system involving Caspase 3 (CASP3) and its 35 inhibitors possessing a common isatin sulfonamide pharmacophore. ![]() 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 ınference system (ANFIS), which are highly promising prediction methods in drug design. 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 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.
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