The Use of Random Forest to Predict Binding Affinity in Docking

摘要

Docking is a structure-based computational tool that can be used to predict the strength with which a small ligand molecule binds to a macromolecular target. Such binding affinity prediction is crucial to design molecules that bind more tightly to a target and thus are more likely to provide the most efficacious modulation of the target's biochemical function. Despite intense research over the years, improving this type of predictive accuracy has proven to be a very challenging task for any class of method. New scoring functions based on non-parametric machine-learning regression models, which are able to exploit effectively much larger volumes of experimental data and circumvent the need for a predetermined functional form, have become the most accurate to predict binding affinity of diverse protein-ligand complexes. In this focused review, we describe the inception and further development of RF-Score, the first machine-learning scoring function to achieve a substantial improvement over classical scoring functions at binding affinity prediction. RF-Score employs Random Forest (RF) regression to relate a structural description of the complex with its binding affinity. This overview will cover adequate benchmarking practices, studies exploring optimal intermolecular features, further improvements and RF-Score software availability including a user-friendly docking webserver and a standalone software for rescoring docked poses. Some work has also been made on the application of RF-Score to the related problem of virtual screening. Comprehensive retrospective virtual screening studies of RF-based scoring functions constitute now one of the next research steps.