Gaussian Processes Regression for Cyclodextrin Host-Guest Binding Prediction

Abstract Machine Learning (ML) techniques are becoming an integral part of rational drug design and discovery. Data-driven modeling regularly outperforms physics-based models for predicting molecular binding affinities, placing ML as a promising tool. Cyclodextrins are nano-cages used to improve the delivery of insoluble or toxic drugs. Due to chemical similarity to proteins, ML approaches could vastly profit to improve affinity prediction and enhance their carriable drug portfolio. Here we evaluate the performance of the Gaussian Process Regression (GPR) to predict the binding affinity of cyclodextrin and known ligands. GPR performance is compared with two well-known ML methods - Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGB). We perform hyperparameter tuning through a Random Search strategy. GPR was able to increase the prediction performance when compared to SVR and XGB, leading to better performance to adjust the data ($R^2$ = 0.803) with low prediction errors (RMSE = 1.811 kJ/mol and MAE = 1.201 kJ/mol).