Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D

Title:Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D

Volume: 12 Issue: 17

Author(s): Irina Kufareva, Yu-Chen Chen, Andrey V. Ilatovskiy and Ruben Abagyan

Affiliation:

Keywords: 3D ligand activity model, atomic property fields, docking, screening

Abstract: Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.

Cite this article as:

Kufareva Irina, Chen Yu-Chen, V. Ilatovskiy Andrey and Abagyan Ruben, Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D, Current Topics in Medicinal Chemistry 2012; 12 (17) . https://dx.doi.org/10.2174/156802612804547335