Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sets
2.2. Data Preprocessing
2.3. Feature Calculation
2.4. Experimental Approaches
2.4.1. Chemicals and Reagents
2.4.2. Compound Purity Checks
2.4.3. Cell Culture
2.4.4. Cell Viability
2.4.5. Plasmid Constructs
2.4.6. Transient Transfections, Promoter Reporter Gene, and Mammalian Two-Hybrid Assays
2.4.7. RNA Preparation and Reverse Transcription Quantitative Real-Time PCR Analysis
3. Results and Discussion
3.1. Analysis of the Data Available for Model Development
3.2. Model Development and Internal Validation
3.3. Analysis of Feature Importance
3.4. Prospective Screening for PXR Activators
- High confidence in predictions: Any selected compound must be predicted as a PXR activator by all of the three RF and three SVM models.
- Novelty: The selected compounds must be structurally distinct to any known PXR ligands. This means that, at the time of selection, chemical structure similarity searches with CAS Scifinder did not result in the retrieval of any known, structurally related PXR ligands. More specifically, a minimum similarity threshold of 70 was used for the searches in CAS Scifinder, meaning that the platform would report literature even for rather distantly related PXR agonists.
- Purchasability: The selected compounds must be available from MolPort in sufficient quantities (5 mg) and at moderate costs.
Experimental Validation
3.5. Hit Follow-Up and SAR Analysis
3.6. Characterization of Prototypical Compounds
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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PubChem PXR Data Set | ToxCast PXR Data Set | Literature PXR Data Set | |
---|---|---|---|
Initial data set | 2864 | 3626 | 434 |
Missing identifier (CID/CAS number) or SMILES | 14 | 308 | 0 |
Inconclusive activity | 488 | 776 | 0 |
MW ≤ 200 Da | 1219 | 1339 | 16 |
Presence of inorganic elements | 23 | 2 | 0 |
Contradicting class labels | 24 | 5 | 0 |
Duplicates | 155 | 17 | 9 |
Final PXR data set | 941 (A: 202, N: 739) 1 | 1179 (A: 642, N: 537) | 409 (A: 250, N: 159) |
PubChem PXR Data Set | ToxCast PXR Data Set | Literature PXR Data Set | |
---|---|---|---|
No. compounds | 941 | 1179 | 409 |
No. scaffolds | 387 | 470 | 259 |
No. drugs scaffolds | 187 (4%) 1 | 224 (4%) | 145 (3%) |
No. cosmetics scaffolds | 108 (15%) | 154 (22%) | 36 (5%) |
No. pesticides scaffolds | 170 (25%) | 171 (25%) | 51 (7%) |
Model | Cross-Validation on the PubChem PXR Data Set | Testing on the | ||||||
---|---|---|---|---|---|---|---|---|
ToxCast PXR Data Set | Literature PXR Data Set | |||||||
ML Algorithm | Feature Set(s) | Gap-Penalization 2 | MCC 3 | AUC 3 | MCC | AUC | MCC | AUC |
RF | PC | no | 0.43 (±0.04) | 0.83 (±0.04) | 0.41 | 0.82 | 0.10 | 0.59 |
yes | 0.43 (±0.05) | 0.80 (±0.04) | 0.46 | 0.81 | 0.21 | 0.57 | ||
FP | no | 0.45 (±0.07) | 0.82 (±0.03) | 0.25 | 0.75 | 0.09 | 0.55 | |
yes | 0.31 (±0.04) | 0.76 (±0.01) | 0.28 | 0.72 | 0.02 | 0.55 | ||
PC + FP | no | 0.48 (±0.06) | 0.83 (±0.03) | 0.35 | 0.81 | 0.03 | 0.56 | |
yes | 0.42 (±0.06) | 0.81 (±0.03) | 0.47 | 0.82 | 0.24 | 0.59 | ||
SVM | PC | no | 0.41 (±0.06) | 0.80 (±0.03) | 0.40 | 0.77 | 0.14 | 0.56 |
yes | 0.41 (±0.06) | 0.80 (±0.02) | 0.44 | 0.81 | 0.13 | 0.56 | ||
FP | no | 0.46 (±0.07) | 0.82 (±0.03) | 0.31 | 0.77 | 0.00 | 0.54 | |
yes | 0.32 (±0.06) | 0.77 (±0.03) | 0.34 | 0.74 | 0.06 | 0.52 | ||
PC + FP | no | 0.48 (±0.10) | 0.86 (±0.03) | 0.44 | 0.82 | 0.00 | 0.55 | |
yes | 0.42 (±0.03) | 0.81 (±0.02) | 0.45 | 0.82 | 0.15 | 0.55 |
ID | Hit Compounds | Nearest Neighbor in Training Set | Similarity of Hit Compound to Nearest Neighbor | Cluster ID | Measured Activity 1 | S.D. 2 |
---|---|---|---|---|---|---|
1 | MolPort-001-946-370 | PubChem | 0.57 | A | 0.15 | 1.09 |
2 | MolPort-009-220-213 | ToxCast | 0.52 | A | 1.41 | 1.59 |
3 | MolPort-003-820-268 | PubChem | 0.47 | A | 9.41 | 4.72 |
4 | MolPort-001-823-879 | ToxCast | 0.51 | A | 1.59 | 1.66 |
5 | MolPort-001-529-219 | PubChem | 0.48 | A | 1.10 | 0.02 |
6 | MolPort-001-545-599 | PubChem | 0.45 | A | 2.78 | 0.80 |
7 | MolPort-000-993-714 | PubChem | 0.54 | A | 8.65 | 7.57 |
8 | MolPort-000-993-856 | PubChem | 0.59 | A | 4.25 | 1.97 |
9 | MolPort-001-838-155 | PubChem | 0.71 | A | 32.77 | 13.01 |
10 | MolPort-002-238-843 | PubChem | 0.52 | A | 5.88 | 0.38 |
11 | MolPort-000-279-714 | ToxCast | 0.47 | A | 33.00 | 19.54 |
12 | MolPort-020-102-538 | ToxCast | 0.53 | B | 17.00 | 6.15 |
13 | MolPort-027-674-395 | ToxCast | 0.44 | B | 28.00 | 4.97 |
14 | MolPort-027-691-387 | ToxCast | 0.37 | B | 0.34 | 2.18 |
15 | MolPort-027-933-109 | ToxCast | 0.52 | B | 26.50 | 8.41 |
16 | MolPort-035-741-775 | ToxCast | 0.44 | B | 4.10 | 0.46 |
17 | MolPort-003-881-027 | ToxCast | 0.33 | B | 44.80 | 14.97 |
18 | MolPort-003-881-070 | ToxCast | 0.34 | B | 39.63 | 19.31 |
19 | MolPort-003-881-625 | ToxCast | 0.32 | B | 9.54 | 1.57 |
20 | MolPort-002-237-349 | ToxCast | 0.34 | B | 4.83 | 2.00 |
21 | MolPort-000-196-136 | ToxCast | 0.34 | B | 7.21 | 3.28 |
22 | MolPort-000-431-925 | ToxCast | 0.26 | singleton | 91.00 | 36.91 |
23 | MolPort-000-690-666 | PubChem | 0.30 | singleton | 11.08 | 5.40 |
24 | MolPort-005-280-909 | ToxCast | 0.58 | singleton | 1.34 | 0.71 |
25 | MolPort-006-630-224 | PubChem | 0.36 | singleton | 136.87 | 28.01 |
26 | MolPort-001-619-443 | ToxCast | 0.46 | singleton | 20.11 | 9.51 |
27 | MolPort-002-547-842 | PubChem | 0.39 | singleton | 10.14 | 1.55 |
28 | MolPort-002-656-531 | PubChem | 0.31 | singleton | 0.34 | 0.63 |
29 | MolPort-001-991-071 | PubChem | 0.47 | singleton | 4.49 | 1.11 |
30 | MolPort-004-004-399 | PubChem | 0.29 | singleton | 0.02 | 1.81 |
31 | MolPort-007-570-291 | ToxCast | 0.41 | singleton | 15.32 | 14.08 |
| |||||
ID | R1 | R2 | R3 | Measured Activity 1 | S.D. 2 |
32 MolPort-006-630-016 | CF3 | H | | 13.19 | 4.02 |
33 MolPort-006-630-300 | CF3 | H | | 37.32 | 5.57 |
34 MolPort-006-629-818 | CF3 | H | | 100.91 | 15.25 |
35 MolPort-006-630-013 | CF3 | H | | 56.85 | 7.23 |
36 MolPort-006-629-816 | CF3 | H | | 12.59 | 4.53 |
37 MolPort-006-629-973 | CF3 | H | | 4.79 | 1.33 |
38 MolPort-006-630-273 | H | OCH2CH3 | | 14.11 | 2.52 |
39 MolPort-006-630-110 | H | F | | 80.37 | 25.75 |
40 MolPort-006-630-237 | H | | | 6.43 | 1.57 |
| |||||||
---|---|---|---|---|---|---|---|
ID | R1 | R2 | R3 | R4 | R5 | Measured Activity 1 | S.D. 2 |
41 MolPort-003-179-500 | H | H | H | COH | H | 6.27 | 0.87 |
42 MolPort-000-431-927 | H | H | Cl | Me | Cl | 115.84 | 17.76 |
43 MolPort-000-431-934 | H | H | Me | Me | Me | 71.67 | 15.93 |
44 * MolPort-020-176-525 | Me | Me | H | Me | H | 215.49 | 31.07 |
45 MolPort-004-827-215 | Me | Me | H | 4-BrC6H4 | H | 9.25 | 4.38 |
| |||||||
---|---|---|---|---|---|---|---|
ID | R1 | R2 | R3 | R4 | Configuration | Measured Activity 1 | S.D. 2 |
46 MolPort-002-173-405 | S | H | | (CH2)2OCH3 | E | 12.45 | 5.06 |
47 MolPort-003-881-006 | S | H | | | Z | 21.96 | 2.87 |
48 MolPort-003-881-001 | S | H | | | Z | 19.72 | 4.24 |
49 MolPort-000-419-900 | S | H | | | Z | 8.54 | 2.04 |
50 MolPort-003-880-748 | S | OCH3 | | | Z | 6.66 | 1.30 |
51 MolPort-003-881-514 | NH | OCH3 | | H | Z | 4.10 | 1.63 |
52 MolPort-044-415-081 | S | OCH3 | | | E/Z | 15.73 | 4.05 |
53 MolPort-002-216-386 | S | OCH3 | | Prop | Z | 84.18 | 23.30 |
54 MolPort-001-899-455 | S | OCH3 | | Et | E | 19.46 | 8.00 |
55 MolPort-001-552-801 | S | OCH3 | Et | Et | Z | 18.65 | 6.09 |
56 MolPort-039-019-225 | S | OCH2CH3 | Prop | | Z | 22.63 | 10.48 |
57 * MolPort-001-634-394 | S | OCH2CH3 | | Et | Z | 95.59 | 45.14 |
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Hirte, S.; Burk, O.; Tahir, A.; Schwab, M.; Windshügel, B.; Kirchmair, J. Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR. Cells 2022, 11, 1253. https://doi.org/10.3390/cells11081253
Hirte S, Burk O, Tahir A, Schwab M, Windshügel B, Kirchmair J. Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR. Cells. 2022; 11(8):1253. https://doi.org/10.3390/cells11081253
Chicago/Turabian StyleHirte, Steffen, Oliver Burk, Ammar Tahir, Matthias Schwab, Björn Windshügel, and Johannes Kirchmair. 2022. "Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR" Cells 11, no. 8: 1253. https://doi.org/10.3390/cells11081253