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[Preprint]. 2024 Mar 11:2024.03.06.583803.
doi: 10.1101/2024.03.06.583803.

Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling

Kuo Hao Lee  1 , Sung Joon Won  1 , Precious Oyinloye  1 , Lei Shi  1
Affiliations

Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling

Kuo Hao Lee et al. bioRxiv. .

Abstract

The dopamine transporter (DAT) plays a critical role in the central nervous system and has been implicated in numerous psychiatric disorders. The ligand-based approaches are instrumental to decipher the structure-activity relationship (SAR) of DAT ligands, especially the quantitative SAR (QSAR) modeling. By gathering and analyzing data from literature and databases, we systematically assemble a diverse range of ligands binding to DAT, aiming to discern the general features of DAT ligands and uncover the chemical space for potential novel DAT ligand scaffolds. The aggregation of DAT pharmacological activity data, particularly from databases like ChEMBL, provides a foundation for constructing robust QSAR models. The compilation and meticulous filtering of these data, establishing high-quality training datasets with specific divisions of pharmacological assays and data types, along with the application of QSAR modeling, prove to be a promising strategy for navigating the pertinent chemical space. Through a systematic comparison of DAT QSAR models using training datasets from various ChEMBL releases, we underscore the positive impact of enhanced data set quality and increased data set size on the predictive power of DAT QSAR models.

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Conflict of interest statement

Declarations of Competing Interests No potential conflict of interest was reported by the authors.

Figures

Figure 1.
Figure 1.. Statistics of DAT activity data from ChEMBL33.
The DAT data set was queried and retrieved from entries in a locally installed instance of ChEMBL 33 (May 2023 release). We employed the same query criteria as in our previous study to filter the DAT pharmacological data set. Number of publications on DAT ligands (A), number of related pharmacological activity data (B) and number of unique DAT ligands (C) were plotted along the years. If a compound has <0.999 pairwise similarity to all the other compound in the data set, we consider it as a unique compound. For panel C, the new unique compound in the current year is colored wheat, and the unique compounds found in previous years is in light grey. The total number of publications, pharmacological activity data, and unique compounds are 773, 7815, and 6689 respectively. The data sets in each panel are separated by dash lines between 2020 and 2021, to indicated what we observed that the curation in ChEMBL can be delayed for more than 2 years.
Figure 2.
Figure 2.. Clustering of representative DAT ligands.
For a total of 61 well-known DAT inhibitors collected from literature, , and DrugBank. To characterize these DAT ligands, we employed the hierarchical clustering approach implemented in the Schrodinger Maestro suite (version 2023–3). We used linear fingerprints, Tanimoto similarity, and average linkage method. Note that three pairs of compounds (armodafinil and modafinil, methylphenidate and dexmethylphenidate, and amphetamine and dextroamphetamine) are enantiomers and we only use modafinil, methylphenidate, and amphetamine when carrying out the clustering. Some known scaffolds were also used to adjust the final representative clusters. The single-member clusters are shown in Fig. S1.
Figure 3.
Figure 3.. Comparative analysis of DAT pharmacological data sets with correlation metrics and linear regressions.
The DAT pharmacological data can be divided into four sets, uptake pKi, uptake pIC50, binding pKi, and binding pIC50. The overlapping compounds between different data sets were extracted for comparisons. The correlation of determination (R2) and the Pearson coefficient correlation (Rp), as well as the number of the overlapping compounds (referred as “overlapping”), are indicated at the top left corner of each panel for the indicated comparisons. The red lines are the linear regressions of the indicated data sets; the black dotted lines are the linear regressions with the slope restrained to 1. Note that slope-restrained regression results were not shown for panels B and D, due to poor goodness-of-fit.
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References

    1. Chen N. H.; Reith M. E.; Quick M. W. Synaptic uptake and beyond: the sodium- and chloride-dependent neurotransmitter transporter family SLC6. Pflugers Arch 2004, 447 (5), 519–531. DOI: 10.1007/s00424-003-1064-5 From NLM Medline. - DOI - PubMed
    1. Beuming T.; Shi L.; Javitch J. A.; Weinstein H. A comprehensive structure-based alignment of prokaryotic and eukaryotic neurotransmitter/Na+ symporters (NSS) aids in the use of the LeuT structure to probe NSS structure and function. Mol Pharmacol 2006, 70 (5), 1630–1642. DOI: 10.1124/mol.106.026120. - DOI - PubMed
    1. Aggarwal S.; Mortensen O. V. Overview of Monoamine Transporters. Curr Protoc Pharmacol 2017, 79, 12 16 11–12 16 17. DOI: 10.1002/cpph.32 From NLM Medline. - DOI - PMC - PubMed
    1. Juarez Olguin H.; Calderon Guzman D.; Hernandez Garcia E.; Barragan Mejia G. The Role of Dopamine and Its Dysfunction as a Consequence of Oxidative Stress. Oxid Med Cell Longev 2016, 2016, 9730467. DOI: 10.1155/2016/9730467 From NLM Medline. - DOI - PMC - PubMed
    1. Stolzenberg S.; Quick M.; Zhao C.; Gotfryd K.; Khelashvili G.; Gether U.; Loland C. J.; Javitch J. A.; Noskov S.; Weinstein H.; Shi L. Mechanism of the Association between Na+ Binding and Conformations at the Intracellular Gate in Neurotransmitter:Sodium Symporters. J Biol Chem 2015, 290 (22), 13992–14003. DOI: 10.1074/jbc.M114.625343. - DOI - PMC - PubMed

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