Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design


Erdas-Cicek O., Atac A. O. , Gurkan-Alp A. S. , Buyukbingol E., ALPASLAN F. N.

JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol.59, no.11, pp.4654-4662, 2019 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 59 Issue: 11
  • Publication Date: 2019
  • Doi Number: 10.1021/acs.jcim.9b00206
  • Title of Journal : JOURNAL OF CHEMICAL INFORMATION AND MODELING
  • Page Numbers: pp.4654-4662

Abstract

Understanding the interaction between drug molecules and proteins is one of the main challenges in drug design. Several tools have been developed recently to decrease the complexity of the process. Artificial intelligence and machine learning methods offer promising results in predicting the binding affinities. It becomes possible to do accurate predictions by using the known protein-ligand interactions. In this study, the electrostatic potential values extracted from 3-dimensional grid cubes of the drug-protein binding sites are used for predicting binding affinities of related complexes. A new algorithm with a dynamic feature selection method was implemented, which is derived from Compressed Images For Affinity Prediction (CIFAP) study, to predict binding affinities of Checkpoint Kinase 1 and Caspase 3 inhibitors.