Generation of 2D-QSAR Model for Angiogenin Inhibitors: A Ligand-Based Approach for Cancer Drug Design
Krishnan Sundar, Joseph Christina Rosy, Saminathan Balamurali, John Asnet Mary, Rajaiah Shenbagara · Trends in Bioinformatics · 2016
Background and Objective: Angiogenin is a monomeric protein which has been considered as an important factor in angiogenesis.Recent studies on angiogenin proved that it is an ideal drug target for treating cancer and vascular dysfunctions.The present study aimed to develop a Quantitative Structure Activity Relationship (QSAR) model with small molecules of angiogenin inhibitors.Methodology: The small molecule inhibitors were divided into training and test sets to build the QSAR model.Multiple Linear Regression (MLR) and Partial Least Square (PLS) methods were used to develop QSAR models.Results: In the MLR model, the descriptors generated for the compounds showed multicollinearity and resulted in a mono-parametric equation.The model generated by PLS satisfied both internal and external cross validation parameters.The predicted model showed the positive contribution of ring atoms and donor hydrogen bonds to the activity.Conclusion: As these parameters are reported to be crucial for biological activity of drugs, it can be used to do develop effective small molecule drug candidates for angiogenin.