Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds
Abstract
:1. Introduction
2. Results and Discussion
2.1. General Modeling Approach
2.2. Mycobacterium tuberculosis Inhibitor Permeability Dataset
2.3. Molecular Descriptors
2.4. Neural Network Modeling Procedure
2.5. Predictive Model of Mycobacterium tuberculosis Permeability
3. Materials and Methods
3.1. Mycobacterium tuberculosis Inhibitor Permeability Dataset
3.2. Modeling Workflow
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Sample Availability
References
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Dataset | MtbPen8242 | MtbPen5371ad | |||
---|---|---|---|---|---|
Predicted | Predicted | ||||
Positive | Negative | Positive | Negative | ||
Observed | Positive | 1435 | 1236 | 2214 | 457 |
Negative | 528 | 5043 | 437 | 2263 |
AID 1 | ID | Type | Activity/Compound Count 2 | Description | Activity Condition 3 |
---|---|---|---|---|---|
1332 | C01 | Cell | 1118 | High throughput screen to identify inhibitors of Mycobacterium tuberculosis H37Rv | Inh30 |
1626 | C02 | Cell | 215,397 | High throughput screen to identify inhibitors of Mycobacterium tuberculosis H37Rv | Inh30 |
1949 | C03 | Cell | 100,697 | High throughput screen of 100,000 compound library to identify inhibitors of Mycobacterium tuberculosis H37Rv | Inh30 |
2842 | C04 | Cell | 23,823 | High throughput screen of a putative kinase compound library to identify inhibitors of Mycobacterium tuberculosis H37Rv | Inh30 |
449762 | C05 | Cell | 327,669 | High throughput screening assay used to identify novel compounds that inhibit Mycobacterium tuberculosis in 7H9 media | Inh30 |
1259343 | C06 | Cell | 6225 | High throughput screening of small molecules that kill Mycobacterium tuberculosis | Inh30 |
1259417 | C07 | Cell | 1105 | High throughput whole cell screen to identify inhibitors of Mycobacterium tuberculosis | Inh30 |
1671161 | C08 | Cell | 96,022/86,588 | Phenotypic growth assay for Mycobacterium tuberculosis grown for 4 days on DPPC, cholesterol, tyloxapol-based media | Inh30 |
1671162 | C09 | Cell | 103,984/86,574 | Phenotypic growth assay for Mycobacterium tuberculosis grown for 3 days on 7H9, glucose tyloxapol-based media | Inh30 |
1671174 | C10 | Cell | 53,171/53,165 | Phenotypic assay to identify agents that inhibit growth of Mycobacterium tuberculosis | Inh30 |
488890 | C11 | Cell | 324,545 | Elucidation of physiology of non-replicating, drug-tolerant Mycobacterium tuberculosis | Inh30 |
375 | T01 | Target | 10,011/10,009 | Mycobacterium tuberculosis pantothenate synthetase assay | Outcome |
1376 | T02 | Target | 216,162/215,860 | Inhibitors of mycobacterial glucosamine-1-phosphate acetyl transferase (GlmU) | Outcome |
2606 | T03 | Target | 324,858/324,747 | Primary biochemical high throughput screening assay to identify inhibitors of the membrane-associated serine protease Rv3671c in M. tuberculosis | Outcome |
504406 | T04 | Target | 324,148/324,048 | High throughput screening of inhibitors of Mycobacterium tuberculosis UDP-galactopyranose mutase (UGM) enzyme | Outcome |
540299 | T05 | Target | 103,205/102,628 | A screen for compounds that inhibit the MenB enzyme of Mycobacterium tuberculosis | Outcome |
588335 | T06 | Target | 356,407/356,160 | Counterscreen for inhibitors of the fructose-bisphosphate aldolase (FBA) of M. tuberculosis | Outcome |
602481 | T07 | Target | 356,486/353,572 | Mycobacterium tuberculosis BioA enzyme inhibitor | Outcome |
1159583 | T08 | Target | 301,203/300,060 | High throughput screen for small molecule inhibitors of a hypoxia-regulated fluorescent biosensor in Mycobacterium tuberculosis | Outcome |
1671160 | T09 | Target | 8874/8841 | Assay for Asp RNA synthetase-1 from Mycobacterium tuberculosis | Inh30 |
1671178 | T10 | Target | 67,199/66,591 | Mycobacterium tuberculosis polyketide synthase 13 thioesterase (PKS13) | Inh30 |
2221 | T11 | Target | 293,466/293,376 | Cell-free homogenous primary high throughput screen to identify inhibitors of RecA intein splicing activity | Outcome |
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Radchenko, E.V.; Antonyan, G.V.; Ignatov, S.K.; Palyulin, V.A. Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds. Molecules 2023, 28, 633. https://doi.org/10.3390/molecules28020633
Radchenko EV, Antonyan GV, Ignatov SK, Palyulin VA. Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds. Molecules. 2023; 28(2):633. https://doi.org/10.3390/molecules28020633
Chicago/Turabian StyleRadchenko, Eugene V., Grigory V. Antonyan, Stanislav K. Ignatov, and Vladimir A. Palyulin. 2023. "Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds" Molecules 28, no. 2: 633. https://doi.org/10.3390/molecules28020633
APA StyleRadchenko, E. V., Antonyan, G. V., Ignatov, S. K., & Palyulin, V. A. (2023). Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds. Molecules, 28(2), 633. https://doi.org/10.3390/molecules28020633