Machine learning methods for developments of binding kinetic models in predicting protein‐ligand dissociation rate constants

Abstract

Abstract Binding kinetic properties of protein–ligand complexes are crucial factors affecting the drug potency. Nevertheless, the current in silico techniques are insufficient in providing accurate and robust predictions for binding kinetic properties. To this end, this work develops a variety of binding kinetic models for predicting a critical binding kinetic property, dissociation rate constant, using eight machine learning (ML) methods (Bayesian Neural Network (BNN), partial least squares regression, Bayesian ridge, Gaussian process regression, principal component regression, random forest, support vector machine, extreme gradient boosting) and the descriptors of the van der Waals/electrostatic interaction energies. These eight models are applied to two case studies involving the HSP90 and RIP1 kinase inhibitors. Both regression results of two case studies indicate that the BNN model has the state‐of‐the‐art prediction accuracy (HSP90: Rtest2=0.947, MAEtest = 0.184, rtest = 0.976, RMSEtest = 0.220; RIP1 kinase: Rtest2=0.745, MAEtest = 0.188, rtest = 0.961, RMSEtest = 0.290) in comparison with other seven ML models.

Keywords

• Bayesian neural network
• binding kinetics
• dissociation rate constant
• machine learning
• protein‐ligand interaction energies