A Two-Step Target Binding and Selectivity Support Vector Machines Approach for Virtual Screening of Dopamine Receptor Subtype-Selective Ligands

Abstract

Target selective drugs, such as dopamine receptor (DR) subtype selective ligands, are developed for enhanced therapeutics and reduced side effects. In silico methods have been explored for searching DR selective ligands, but encountered difficulties associated with high subtype similarity and ligand structural diversity. Machine learning methods have shown promising potential in searching target selective compounds. Their target selective capability can be further enhanced. In this work, we introduced a new two-step support vector machines target-binding and selectivity screening method for searching DR subtype-selective ligands, which was tested together with three previously-used machine learning methods for searching D1, D2, D3 and D4 selective ligands. It correctly identified 50.6%–88.0% of the 21–408 subtype selective and 71.7%–81.0% of the 39–147 multi-subtype ligands. Its subtype selective ligand identification rates are significantly better than, and its multi-subtype ligand identification rates are comparable to the best rates of the previously used methods. Our method produced low false-hit rates in screening 13.56 M PubChem, 168,016 MDDR and 657,736 ChEMBLdb compounds. Molecular features important for subtype selectivity were extracted by using the recursive feature elimination feature selection method. These features are consistent with literature-reported features. Our method showed similar performance in searching estrogen receptor subtype selective ligands. Our study demonstrated the usefulness of the two-step target binding and selectivity screening method in searching subtype selective ligands from large compound libraries.

Structure‐Based Virtual Screening for Dopamine D2 Receptor Ligands as Potential Antipsychotics

“…From these2 1c ompounds tested, ten D 2 ligandsw ere identified (47.6 %s uccess rate, among them D 2 receptor antagonists, as expected) that have additional affinity foro ther receptors tested,i np articular5 -HT 2A receptors. [13][14][15] Moreover,v irtual screening was used to identify ligandso ft he dopamine D 3 receptor,aclose homologue of the dopamine D 2 receptor. We found one D 2 receptor antagonist that did not have ap rotonatablen itrogen atom, which is ak ey structurale lemento ft he classical D 2 pharmacophore model necessary for interactionw ith the conserved Asp(3.32)r esidue.…”

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

“…In fact, QSAR played an indispensable role in GPCR subtype selective ligand design1415, e.g., ARs16, dopamine receptors17, serotonin receptors 5HT1E/5HT1F18 and cannabinoid receptor CB1/CB21920. For AR ligands, Michelan et al .…”

Virtual Screening Methods as Tools for Drug Lead Discovery from Large Chemical Libraries

“…Examples of recent efforts for improving VS algorithms include adding pharmacophoric shims for generating highly predictive targetcustomized docking models [65,66], most-frequent-feature guided development of pharmacophore models [19], and a two-step target binding and selectivity screening approach for searching target subtype selective ligands [67]. Examples of recent efforts for improving VS algorithms include adding pharmacophoric shims for generating highly predictive targetcustomized docking models [65,66], most-frequent-feature guided development of pharmacophore models [19], and a two-step target binding and selectivity screening approach for searching target subtype selective ligands [67].…”