Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning MethodsA Case Study of Serotonin Receptors 5‑HT₆ and 5‑HT₇

Molecular
docking, despite its undeniable usefulness in computer-aided
drug design protocols and the increasing sophistication of tools used
in the prediction of ligand–protein interaction energies, is
still connected with a problem of effective results analysis. In this
study, a novel protocol for the automatic evaluation of numerous docking
results is presented, being a combination of Structural Interaction
Fingerprints and Spectrophores descriptors, machine-learning techniques,
and multi-step results analysis. Such an approach takes into consideration
the performance of a particular learning algorithm (five machine learning
methods were applied), the performance of the docking algorithm itself,
the variety of conformations returned from the docking experiment,
and the receptor structure (homology models were constructed on five
different templates). Evaluation using compounds active toward 5-HT6 and 5-HT7 receptors, as well as additional analysis
carried out for beta-2 adrenergic receptor ligands, proved that the
methodology is a viable tool for supporting virtual screening protocols,
enabling proper discrimination between active and inactive compounds.