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 three well-known ML methods—Support Vector Regression (SVR), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB)—to predict the binding affinity of cyclodextrin and known ligands. We perform hyperparameter tuning through Random Search. The results were compatible with the presented literature. We increased our previous prediction performance and present a GPR model to adjust to the data (\(R^2\) = 0.803) with low prediction errors (RMSE = 1.811 kJ/mol and MAE = 1.201 kJ/mol).