Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development
Abstract
:1. Introduction
2. Virtual Screening
2.1. Virtual Screening Overview
2.2. Structure-Based Virtual Screening
2.3. Ligand-Based Virtual Screening
3. Biological Property Prediction and Hit Filtering
4. Hit Compound Optimization
4.1. Structure-Based Hit Compound Optimization
4.2. Ligand-Based Hit Compound Optimization
5. Limitations and Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Target | # of Compounds/ Compound Library | Hit Rate a | Binding Affinity of Hits | Literature |
---|---|---|---|---|---|
Structure-based virtual screening | |||||
Docking | μ-opioid receptor | 3 M/ZINC | 23/23 | 2.3–14 μM | Manglik et al., 2016 [16] |
Docking | Mas-related G protein- coupled receptor X2 (MRGPRX2) | 3.7 M/ZINC | 20/20 | <10 μM | Lansu et al., 2017 [17] |
Docking | Histamine H1 receptor | 100 K/ZINC | 19/26 (73%) | 6 nM–10 μM | De Graaf et al., 2011 [18] |
Docking | Histamine H4 receptor | 8.7 M/ZINC | 16/255 (6%) | 85–1480 nM | Kiss et al., 2008 [19] |
Docking | Histamine H4 receptor | 7 K/Bioprojet chemical library | 28/120 (23%) | 4 nM–16 μM | Levoin et al., 2017 [20] |
Docking | Melanin-concentrating hormone receptor 1 (MCH-R1) | 187 K/In-house collection [21] | 6/129 (5%) | 7–20 μM | Cavasotto et al., 2008 [22] |
Docking | Chemokine receptor CCR5 | 1.6 M/8 vendors | 10/59 (17%) | 5–200 μM | Kellenberger et al., 2007 [23] |
Docking | Adenosine receptor A2A | 1.4 M/ZINC | 7/20 (35%) | 200 nM–9 μM | Carlsson et al., 2010 [24] |
Docking | Adenosine receptor A2A | 4.3 M/Molsoft ScreenPub | 23/56 (41%) | <10 μM | Katritch et al., 2010 [25] |
Docking | β2-adrenergic receptor | 1 M/ZINC | 6/25 (24%) | <4 μM | Kolb et al., 2009 [26] |
Docking | Dopamine D2 receptor | 6.5 M/Enamine | 10/21 (48%) | 58 nM–25 μM | Kaczor et al., 2016 [27] |
Docking | Choline acetyltransferase (ChAT) | 300 K/Asinex Gold and Platinum collection library | 3/35 (9%) | 7–26 μM | Kumar et al., 2017 [28] |
Docking | Tau fibrils | 62 K/FDA-approved small molecule drugs and ChemBridge CNS-set | 4/46 (9%) | <5 μM | Seidler et al., 2022 [29] |
Docking | Dopamine D3 receptor | 1.5 M/ChemDiv | 27/37(73%) | <10 μM | Jin et al., 2023 [30] |
Pharmacophore | Formylpeptide receptor (FPR) | 480 K/Chemical Diversity Laboratories [31] | 30/4324 (0.7%) | 1–32 μM | Edwards et al., 2005 [32] |
Pharmacophore | complement component 3a receptor 1 (C3AR1) | -/In-house collection | 4/157 (3%) | <10 μM | Klabunde et al., 2009 [33] |
Pharmacophore | Alpha-synuclein fibrils | 10 M/ZINC15 | 2/17 (12%) | 10–490 nM | Ferrie et al., 2020 [2] |
Pharmacophore | Histamine H4 receptor | 22 M/ZINC12 | 3/291 (1%) | <10 μM | Ko et al., 2018 [34] |
Pharmacophore Docking | Sphingosine kinase 1 (SphK1) | 147/Custom-selected Library | 3/16 (19%) | 12–60 μM | Vettorazzi et al., 2017 [35] |
Pharmacophore Docking | Serotonin transporter (SERT) | 1 M/ZINC | 2/15 (13%) | 17–38 μM | Manepalli et al., 2011 [36] |
Pharmacophore Docking | Thyrotropin-releasing hormone receptor1 (TRH-R1) | 1 M/ZINC | 100/100 | Sub μM–μM | Engel et al., 2008 [37] |
Pharmacophore Docking | Alpha1A adrenergic receptor | 23 K/MDL Drug Data Report | 37/80 (46%) | <10 μM | Evers et al., 2005 [38] |
Pharmacophore Docking | Neurokinin-1 (NK1) receptor | 827 K/7 databases | 1/7 (14%) | 0.25 μM | Evers et al., 2004 [39] |
Machine learning | Acetylcholinesterase (AchE) | 15 M/Enamine REAL database | 10/23(43%) | <50 μM | Adeshina et al., 2020 [40] |
Ligand-based virtual screening | |||||
Pharmacophore | Metabotropic glutamate receptor 5 (mGluR5) | 194 K/Asinex Gold compound collection | 9/27 (33%) | <70 μM | Renner et al., 2005 [41] |
Pharmacophore | Metabotropic glutamate receptor 1 (mGluR1) | 201 K/Asinex Gold Collection | 6/23 (26%) | 0.75–>40 μM | Noeske et al., 2007 [42] |
2D-QSAR | Sigma 2 receptor | 2 K/DrugBank | 10/34 (29%) | 140 nM–μM | Yu et. al., 2021 [43] |
2D Fingerprint | Sigma 2 receptor | 47 M/MCule Inc. | 12/46 (26%) | 0.6–700 nM | Kim et al., 2022 [3] |
Ligand- and structure-based virtual screening | |||||
2D/3D-QSAR Docking | Sigma 2 receptor | 1517/Seaweed Metabolite and ChEBI | 15/15 | 0.6–5.3 nM | Floresta et al., 2018 [44] |
2D Fingerprint Pharmacophore | Melanin-concentrating hormone 1 receptor (MCH-1) | 615 K/24 Vendors | 15/795 (1.9%) | 1–30 μM | Clark et al., 2004 [21] |
Similarity Pharmacophore Docking | Free fatty acid receptor 1 (FFAR1) | 2.6 M/ZINC | 6/52 (12%) | <10 μM | Tikhonova et al., 2008 [45] |
Pharmacophore Docking | Subtype six serotonin receptor (5-HT6) | -/Princeton BM and ChemBridge | 14/92 (15%) | <1 μM | Staron et al., 2020 [46] |
Pharmacophore Docking | 5-HT7 receptor (5-HT7R) | 730 K/Enamine Screening Collection | 2/26 (8%) | 197–265 nM | Kurczab et al., 2010 [47] |
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Hsieh, C.-J.; Giannakoulias, S.; Petersson, E.J.; Mach, R.H. Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development. Pharmaceuticals 2023, 16, 317. https://doi.org/10.3390/ph16020317
Hsieh C-J, Giannakoulias S, Petersson EJ, Mach RH. Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development. Pharmaceuticals. 2023; 16(2):317. https://doi.org/10.3390/ph16020317
Chicago/Turabian StyleHsieh, Chia-Ju, Sam Giannakoulias, E. James Petersson, and Robert H. Mach. 2023. "Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development" Pharmaceuticals 16, no. 2: 317. https://doi.org/10.3390/ph16020317