title: Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods--A Case Study of Serotonin Receptors 5-HT(6) and 5-HT(7).
abstract: 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.
authors: †Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland.|‡Faculty of Chemistry, Jagiellonian University, 3 Ingardena Street, 30-060 Kraków, Poland.
authors: Sabina
authors: S
authors: Smusz
authors: †Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland.
authors: Stefan
authors: S
authors: Mordalski
authors: †Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland.
authors: Jagna
authors: J
authors: Witek
authors: †Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland.
authors: Krzysztof
authors: K
authors: Rataj
authors: †Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland.
authors: Rafał
authors: R
authors: Kafel
authors: †Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland.
authors: Andrzej J
authors: AJ
authors: Bojarski
mesh_terms: D000465:Algorithms; D001331:Automation; D008024:Ligands; D000069550:Machine Learning; D062105:Molecular Docking Simulation; D011487:Protein Conformation; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry / Q000378:metabolism; D011985:Receptors, Serotonin / Q000737:chemistry / Q000378:metabolism*
chemical_list: D008024:Ligands; D018343:Receptors, Adrenergic, beta-2; D011985:Receptors, Serotonin; C097216:serotonin 6 receptor; C104425:serotonin 7 receptor