Title:The Quantitative Structure-Activity Relationships between GABAA Receptor
and Ligands based on Binding Interface Characteristic
Volume: 17
Issue: 6
Author(s): Shu Cheng and Yanrui Ding*
Affiliation:
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122,China
Keywords:
QSAR, GABAA, GBRT, mean decrease impurity, random forests, ligand-receptor interaction characteristics, pIC50.
Abstract:
Background: Quantitative Structure Activity Relationship (QSAR) methods based on
machine learning play a vital role in predicting biological effect.
Objective: Considering the characteristics of the binding interface between ligands and the inhibitory
neurotransmitter Gamma-Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of
ligands that bind to the human GABAA receptor.
Methods: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked
ligand molecular descriptors. Three QSAR models are built using a gradient boosting regression
tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand
receptor interaction characteristics.
Results: The features of the optimal QSAR model contain both the docked ligand molecular descriptors
and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation
(Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing
set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the
pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02
and 0.03, respectively.
Conclusion: We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features,
which can help to build the QSAR model more accurately.