Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery
Open accessAbstract
BACKGROUND: Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. METHODOLOGY/PRINCIPAL FINDINGS: In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10 μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi. CONCLUSIONS/ SIGNIFICANCE: We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Access to a collection of predictive models for many of the known diseases or related targets could help find additional compounds for testing that may have previously been overlooked.
“Access to a collection of predictive models for many of the known diseases or related targets could help find additional compounds for testing that may have previously been overlooked13.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [13]
Our own recent cheminformatics prospective testing efforts have identified compounds active in vitro and in vivo against Chagas disease and the Ebola virus using Bayesian algorithms.
“Several of our own recent cheminformatics prospective testing efforts have identified compounds active in vitro and in vivo against Chagas disease13 and the Ebola virus41 using Bayesian algorithms.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395 · see citing works
Rassi, A., Rassi, A., & Marin‐Neto, J. A. (2010) · The Lancet · doi:10.1016/s0140-6736(10)60061-x · Cited by 2,125 · see citing works
How were new medicines discovered?
Swinney, D. C., & Anthony, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3480 · Cited by 1,812 · see citing works
Control of Neglected Tropical Diseases
Hotez, P. J., Molyneux, D. H., Fenwick, A., Kumaresan, J., Sachs, S. E., Sachs, J. D., & Savioli, L. (2007) · New England Journal of Medicine · doi:10.1056/nejmra064142 · Cited by 1,614 · see citing works
Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., Holland, T. A., Keseler, I. M., Kothari, A., Kubo, A., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1103 · Cited by 1,204 · see citing works
High pressure effects on protein structure and function
Mozhaev, V. V., Heremans, K., Jzn, J. F., Masson, P., & Balny, C. (1996) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(199601)24:1 · Cited by 716 · see citing works
Chagas disease: a new worldwide challenge
Coura, J. R., & Albajar-Viñas, P. (2010) · Nature · doi:10.1038/nature09221 · Cited by 657 · see citing works
Caspi, R. R., Altman, T., Dreher, K. A., Fulcher, C., Subhraveti, P., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1014 · Cited by 603 · see citing works
An Estimate of the Burden of Chagas Disease in the United States
Bern, C., & Montgomery, S. P. (2009) · Clinical Infectious Diseases · doi:10.1086/605091 · Cited by 599 · see citing works
The discovery of first-in-class drugs: origins and evolution
Eder, J., Sedrani, R., & Wiesmann, C. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4336 · Cited by 514 · see citing works
Side effects of benznidazole as treatment in chronic Chagas disease: fears and realities
Viotti, R. J., Vigliano, C., Lococo, B., et al. (2009) · Expert Review of Anti-infective Therapy · doi:10.1586/14787210.7.2.157 · Cited by 329 · see citing works
Bender, A., Scheiber, J., Glick, M., Davies, J. W., Azzaoui, K., Hamon, J., Urbán, L., Whitebread, S., & Jenkins, J. L. (2007) · ChemMedChem · doi:10.1002/cmdc.200700026 · Cited by 324 · see citing works
Chagas Disease Drug Discovery: Toward a New Era
Chatelain, E. (2014) · SLAS DISCOVERY · doi:10.1177/1087057114550585 · Cited by 312 · see citing works
Rogers, D. O. W., Brown, R. D., & Hahn, M. (2005) · SLAS DISCOVERY · doi:10.1177/1087057105281365 · Cited by 297 · see citing works
Peña, I., Manzano, M. P., Cantizani, J., Kessler, A., Alonso-Padilla, J., Bardera, A. I., Álvarez, E. F., Colmenarejo, G., Cotillo, I., Roquero, I., et al. (2015) · Scientific Reports · doi:10.1038/srep08771 · Cited by 262 · see citing works
Chagas Disease: “The New HIV/AIDS of the Americas”
Hotez, P. J., Dumonteil, E., Woc-Colburn, L. E., et al. (2012) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001498 · Cited by 213 · see citing works
Fexinidazole: A Potential New Drug Candidate for Chagas Disease
Bahia, M. T., de Andrade, I. M., Martins, T. A. F., et al. (2012) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001870 · Cited by 185 · see citing works
Cheminformatics analysis and learning in a data pipelining environment
Hassan, M., Brown, R. D., Varma‐O'Brien, S., & Rogers, D. O. W. (2006) · Molecular Diversity · doi:10.1007/s11030-006-9041-5 · Cited by 184 · see citing works
Heby, O., Persson, L., & Rentala, M. (2007) · Amino Acids · doi:10.1007/s00726-007-0537-9 · Cited by 174 · see citing works
Review of pyronaridine anti-malarial properties and product characteristics
Croft, S. L., Duparc, S., Arbe‐Barnes, S., et al. (2012) · Malaria Journal · doi:10.1186/1475-2875-11-270 · Cited by 159 · see citing works
The Pathway Tools cellular overview diagram and Omics Viewer
Paley, S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl334 · Cited by 158 · see citing works
New, Improved Treatments for Chagas Disease: From the R&D Pipeline to the Patients
Ribeiro, I., Sevcsik, A.-M., Alves, F. P., et al. (2009) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0000484 · Cited by 156 · see citing works
Chagas disease: pushing through the pipeline
Clayton, J. (2010) · Nature · doi:10.1038/nature09224 · Cited by 146 · see citing works
Global Phenotypic Screening for Antimalarials
Guiguemde, W. A., Shelat, A. A., Garcia-Bustos, J., Diagana, T. T., Gamo, F., & Guy, R. K. (2012) · Chemistry & Biology · doi:10.1016/j.chembiol.2012.01.004 · Cited by 136 · see citing works
The Pathway Tools Pathway Prediction Algorithm
Karp, P. D., Latendresse, M., & Caspi, R. (2011) · Standards in Genomic Sciences · doi:10.4056/sigs.1794338 · Cited by 132 · see citing works
VNI Cures Acute and Chronic Experimental Chagas Disease
Villalta, F., Dobish, M. C., Nde, P. N., Kleshchenko, Y. Y., Hargrove, T. Y., Johnson, C., Waterman, M. R., Johnston, J. N., & Lepesheva, G. I. (2013) · The Journal of Infectious Diseases · doi:10.1093/infdis/jit042 · Cited by 109 · see citing works
Engel, J. C., Ang, K. K. H., Chen, S., et al. (2010) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01777-09 · Cited by 106 · see citing works
Self-citationSaravanamuthu, A., Vickers, T. J., Bond, C. S., et al. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m403187200 · Cited by 106 · see citing works
Seeding Collaborations to Advance Kinase Science with the GSK Published Kinase Inhibitor Set (PKIS)
Drewry, D. H., Willson, T. M., & Zuercher, W. J. (2014) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026613666131127160819 · Cited by 106 · see citing works
Studies on a new antimalarial compound: pyronaridine
Chen, C., Tang, L.-H., & Jantanavivat, C. (1992) · Transactions of the Royal Society of Tropical Medicine and Hygiene · doi:10.1016/0035-9203(92)90414-8 · Cited by 105 · see citing works
Reversible Cysteine Protease Inhibitors Show Promise for a Chagas Disease Cure
Ndao, M., Beaulieu, C., Black, W. C., et al. (2013) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01855-13 · Cited by 102 · see citing works
Klon, A. E., Lowrie, J. F., & Diller, D. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0601315 · Cited by 102 · see citing works
Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery
Ekins, S., Reynolds, R. C., Kim, H., Koo, M.-S., Ekonomidis, M., Talaue, M. T., Paget, S. D., Woolhiser, L. K., Lenaerts, A. J. M., Bunin, B. A., et al. (2013) · Chemistry & Biology · doi:10.1016/j.chembiol.2013.01.011 · Cited by 97 · see citing works
Self-citationGlobal Bayesian Models for the Prioritization of Antitubercular Agents
Prathipati, P., Ma, N. L., & Keller, T. H. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800143n · Cited by 95 · see citing works
Computational Models for Drug Inhibition of the Human Apical Sodium-Dependent Bile Acid Transporter
Zheng, X., Ekins, S., Raufman, J., et al. (2009) · Molecular Pharmaceutics · doi:10.1021/mp900163d · Cited by 94 · see citing works
Self-citationSynergy Testing of FDA-Approved Drugs Identifies Potent Drug Combinations against Trypanosoma cruzi
Planer, J. D., Hulverson, M. A., Arif, J. A., Ranade, R. M., Don, R., & C, F. S. B. A. (2014) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0002977 · Cited by 93 · see citing works
Safety of benznidazole use in the treatment of chronic Chagas' disease
Hasslocher‐Moreno, A. M., do Brasil, P. E. A. A., de Sousa, A. S., et al. (2012) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dks027 · Cited by 93 · see citing works
Wiggers, H. J., da Rocha, J. R., Fernandes, W. B., Sesti‐Costa, R., Carneiro, Z. A., Cheleski, J., da Silva, A. B. F., Juliano, L., Cezari, M. H. S., Silva, J. S., et al. (2013) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0002370 · Cited by 90 · see citing works
Self-citationBettiol, E., Samanovic, M. I., Murkin, A. S., et al. (2009) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0000384 · Cited by 89 · see citing works
Phenotypic screening, take two
Kotz, J. (2012) · Science-Business eXchange · doi:10.1038/scibx.2012.380 · Cited by 86 · see citing works
Activity In Vivo of Anti-Trypanosoma cruzi Compounds Selected from a High Throughput Screening
Andriani, G., Chessler, A.-D. C., Courtemanche, G., et al. (2011) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001298 · Cited by 84 · see citing works
A collaborative database and computational models for tuberculosis drug discovery
Ekins, S., Bradford, J., Dole, K., et al. (2010) · Molecular BioSystems · doi:10.1039/b917766c · Cited by 83 · see citing works
Self-citationHargrove, T. Y., Wawrzak, Z., Alexander, P. W., Chaplin, J. H., Keenan, M., Charman, S. A., Perez, C. J., Waterman, M. R., Chatelain, E., & Lepesheva, G. I. (2013) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.497990 · Cited by 79 · see citing works
Soeiro, M. D. N. C., de Souza, E. M., da Silva, C. F., et al. (2013) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00070-13 · Cited by 78 · see citing works
Ekins, S., Kaneko, T., Lipinski, C. A., et al. (2010) · Molecular BioSystems · doi:10.1039/c0mb00104j · Cited by 72 · see citing works
Self-citationAlonso-Padilla, J., Cotillo, I., Presa, J. L., et al. (2015) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0003493 · Cited by 66 · see citing works
Choi, S., Park, S.-H., Kim, K. H., et al. (1998) · Anti-Cancer Drugs · doi:10.1097/00001813-199803000-00008 · Cited by 66 · see citing works
Andriani, G., Amata, E., Beatty, J. W., Clements, Z. T., Coffey, B. J., Courtemanche, G., Devine, W. G., Erath, J., Juda, C. E., Wawrzak, Z., et al. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm400012e · Cited by 65 · see citing works
Diverse Inhibitor Chemotypes Targeting Trypanosoma cruzi CYP51
Gunatilleke, S. S., Calvet, C. M., Johnston, J. B., et al. (2012) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001736 · Cited by 63 · see citing works
Anti-malarial efficacy of pyronaridine and artesunate in combination in vitro and in vivo
Vivas, L., Rattray, L., Stewart, L. B., et al. (2007) · Acta Tropica · doi:10.1016/j.actatropica.2007.12.005 · Cited by 61 · see citing works
Essential Metabolites of Mycobacterium tuberculosis and Their Mimics
Lamichhane, G., Freundlich, J. S., Ekins, S., Wickramaratne, N. A., Nolan, S. T., & Bishai, W. R. (2011) · mBio · doi:10.1128/mbio.00301-10 · Cited by 60 · see citing works
Self-citationComputational Approaches That Predict Metabolic Intermediate Complex Formation with CYP3A4 (+b5)
Jones, D. R., Ekins, S., Li, L., & Hall, S. D. (2007) · Drug Metabolism and Disposition · doi:10.1124/dmd.106.014613 · Cited by 60 · see citing works
Self-citationEkins, S., Freundlich, J. S., Hobrath, J. V., White, E. L., & Reynolds, R. C. (2013) · Pharmaceutical Research · doi:10.1007/s11095-013-1172-7 · Cited by 57 · see citing works
Self-citationEkins, S., Reynolds, R. C., Franzblau, S. G., Wan, B., Freundlich, J. S., & Bunin, B. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063240 · Cited by 55 · see citing works
Self-citationTrypanosoma cruzi Clone Dm28c Draft Genome Sequence
Grisard, E. C., Teixeira, S. M. R., de Almeida, L. G. P., et al. (2014) · Genome Announcements · doi:10.1128/genomea.01114-13 · Cited by 53 · see citing works
Chandra, M., Shirani, J., Shtutin, V., Weiss, L. M., Factor, S. M., Petkova, S. B., Rojkind, M., Domı́nguez-Rosales, J. A., Jelicks, L. A., Morris, S. A., et al. (2002) · International Journal for Parasitology · doi:10.1016/s0020-7519(01)00320-4 · Cited by 52 · see citing works
Calvet, C. M., Vieira, D. F., Choi, J. Y., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm500448u · Cited by 50 · see citing works
Montero, A., Vega, C., Marrero‐Ponce, Y., Rolón, M. S., Gómez‐Barrio, A., Escario, J. A., Arán, V. J., Martínez‐Fernández, A. R., & Meneses‐Marcel, A. (2005) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2005.06.049 · Cited by 49 · see citing works
Qi, J., Wang, S., Liu, G., et al. (2004) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2004.05.099 · Cited by 49 · see citing works
Campbell, N. R. C., Loon, J. A. V., Sundaram, R. S., et al. (1987) · Molecular Pharmacology · doi:10.1016/s0026-895x(25)13073-3 · Cited by 49 · see citing works
High Throughput Screening for Anti–Trypanosoma cruzi Drug Discovery
Alonso-Padilla, J., & Rodrı́guez, A. (2014) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0003259 · Cited by 46 · see citing works
Ekins, S., Pottorf, R. S., Reynolds, R. C., Williams, A. J., Clark, A. M., & Freundlich, J. S. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500077v · Cited by 43 · see citing works
Self-citationEkins, S., Freundlich, J. S., & Reynolds, R. C. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500264r · Cited by 41 · see citing works
Self-citationChoy, J. W., Bryant, C., Calvet, C. M., et al. (2013) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.9.3 · Cited by 40 · see citing works
Shtutin, V., Weiss, L. M., Morris, S. A., et al. (2004) · Parasitology Research · doi:10.1007/s00436-004-1080-1 · Cited by 40 · see citing works
Hydroxymethylnitrofurazone Is Active in a Murine Model of Chagas' Disease
Davies, C., Cardozo, R. M., Negrette, O. S., et al. (2010) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01451-09 · Cited by 38 · see citing works
Ekins, S., & Freundlich, J. S. (2011) · Pharmaceutical Research · doi:10.1007/s11095-011-0413-x · Cited by 37 · see citing works
Self-citationBayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis
Ekins, S., Casey, A. C., Roberts, D., et al. (2013) · Tuberculosis · doi:10.1016/j.tube.2013.12.001 · Cited by 37 · see citing works
Self-citationNew ligand-based approach for the discovery of antitrypanosomal compounds
Vega, C., Montero, A., Marrero‐Ponce, Y., et al. (2006) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2005.12.087 · Cited by 34 · see citing works
TB Mobile: a mobile app for anti-tuberculosis molecules with known targets
Ekins, S., Clark, A. M., & Sarker, M. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-13 · Cited by 33 · see citing works
Self-citationClark, A. M., Sarker, M., & Ekins, S. (2014) · Journal of Cheminformatics · doi:10.1186/s13321-014-0038-2 · Cited by 33 · see citing works
Self-citationLead Identification to Clinical Candidate Selection: Drugs for Chagas Disease
Neitz, R. J., Chen, S., Supek, F., et al. (2014) · SLAS DISCOVERY · doi:10.1177/1087057114553103 · Cited by 32 · see citing works
Self-citationEkins, S., Freundlich, J. S., & Reynolds, R. C. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400480s · Cited by 29 · see citing works
Self-citationSarker, M., Talcott, C., Madrid, P. B., et al. (2012) · Pharmaceutical Research · doi:10.1007/s11095-012-0741-5 · Cited by 29 · see citing works
Self-citationCastillo‐Garit, J. A., Vega, C., Rolón, M. S., et al. (2009) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2009.10.007 · Cited by 23 · see citing works
Fakheri, H. T., Bari, Z., & Sardarian, H. (2012) · Helicobacter · doi:10.1111/j.1523-5378.2012.00946.x · Cited by 23 · see citing works
Computational Models for Neglected Diseases: Gaps and Opportunities
Ponder, E. L., Freundlich, J. S., Sarker, M., et al. (2013) · Pharmaceutical Research · doi:10.1007/s11095-013-1170-9 · Cited by 21 · see citing works
Self-citationYe, Z., Dyke, K. V., & Rossan, R. N. (2013) · Malaria Journal · doi:10.1186/1475-2875-12-117 · Cited by 20 · see citing works
Taylor, J. A., Mitchenall, L. A., Rejzek, M., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058010 · Cited by 19 · see citing works
Choi, J. Y., Calvet, C. M., Vieira, D. F., et al. (2014) · ACS Medicinal Chemistry Letters · doi:10.1021/ml500010m · Cited by 19 · see citing works
Beveridge, E., Caldwell, I. C., Latter, V. S., et al. (1980) · Transactions of the Royal Society of Tropical Medicine and Hygiene · doi:10.1016/0035-9203(80)90010-3 · Cited by 17 · see citing works
Effects of Verapamil on Acute Murine Chagas' Disease
Tanowitz, H. B., Wittner, M., Chen, B. Y., et al. (1996) · Journal of Parasitology · doi:10.2307/3283896 · Cited by 16 · see citing works
Identification of small-molecule inhibitors of Trypansoma cruzi replication
Germain, A. R., Carmody, L., Dockendorff, C., et al. (2011) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2011.09.057 · Cited by 15 · see citing works
Posaconazole versus benznidazole for chronic Chagas' disease.
Bestetti, R. B., & Restini, C. B. A. (2014) · PubMed · doi:10.1056/nejmc1407914 · Cited by 13 · see citing works
Carmody, L., Germain, A. R., Engel, J. C., et al. (2013) · Cited by 3 · see citing works
Identification of Small-Molecule Inhibitors of Trypansoma cruzi Infection - Probe 1
Carmody, L., Germain, A. R., Barker, D., et al. (2011) · Europe PMC (PubMed Central) · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [52]Doyle M, McRae JI, Saunders EC, McConville MJ, Likic VA. Summary of Leishmania major, Strain strain Friedlin, version 18.5 2015. http://biocyc.org/LEISH/organism-summary?object=LEISH.
- [64]Ekins S, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, et al A Collaborative Database And Computational Models For Tuberculosis Drug Discovery. Mol BioSystems. 2010; 6: 840– 51. 20567770
- [72]Ekins S, Casey AC, Roberts D, Parish T, Bunin BA. Bayesian Models for Screening and TB Mobile for Target Inference with Mycobacterium tuberculosis Tuberculosis (Edinb). 2014; 94: 162– 69. 24440548 PMC4394018
- [73]Ekins S, Reynolds RC, Franzblau SG, Wan B, Freundlich JS, Bunin BA. Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models PLOSONE. 2013; 8: e63240. PMC3647004 23667592
- [86]Jacoby EM, Schlichting I, Lantwin CB, Kabsch W, Krauth-Siegel RL. Crystal structure of the Trypanosoma cruzi trypanothione reductase.mepacrine complex. Proteins. 1996; 24: 73– 80. 8628734
Cited by
Sorted by citations
Exploiting machine learning for end-to-end drug discovery and development
Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578 · see citing works
Self-citationMachine learning models identify molecules active against the Ebola virus in vitro
Ekins, S., Freundlich, J. S., Clark, A. M., Anantpadma, M., Davey, R. A., & Madrid, P. (2015) · F1000Research · doi:10.12688/f1000research.7217.1 · Cited by 84 · see citing works
Self-citationBoudreau, P. D., Miller, B. W., McCall, L.-I., Almaliti, J., Reher, R., Hirata, K., Le, T., Siqueira-Neto, J. L., Hook, V., & Gerwick, W. H. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b00294 · Cited by 57 · see citing works
Self-citation
- Authors
- Ekins, S., Lage de Siqueira-Neto, J., McCall, L.-I., Sarker, M., Yadav, M., Ponder, E. L., Kallel, E. A., Kellar, D., Chen, S., Arkin, M., Bunin, B. A., McKerrow, J. H., & Talcott, C.
- Affiliations
- Collaborative Drug Discovery (United States) (US)
- University of California San Diego (US)
- SRI International (US)
- Stanford University (US)
- University of California, San Francisco (US)
- Corresponding author
- Ekins, Sean
- ORCID
- Venue
- PLOS Neglected Tropical Diseases
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-06-26
- Volume
- 9
- Issue
- 6
- Pages
- e0003878
- ISSN
- 1935-2727 (ISSN-L)
- 1935-2735
- PMC
- PMC4482694
- OpenAlex
- W822668957
- Cited by
- 93
- References
- 86
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 8 · 2023: 5 · 2022: 9 · 2021: 13 · 2020: 14 · 2019: 11 · 2018: 11 · 2017: 6 · 2016: 5 · 2015: 2
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS neglected tropical diseases (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases (R41AI108003, R41-AI108003-01)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Epidemiology › Trypanosoma species research and implications
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- Trypanosoma cruzi, Chagas disease, drug discovery, Bayesian learning, virtual screening, pathway/genome databases, high-throughput screening, chemical probes, pyronaridine, antimalarial compounds, neglected tropical diseases, drug target prediction, metabolite prediction
- MeSH terms
- Machine Learning, Animals, Bayes Theorem, Cell Line, Disease Models, Animal, Female, Humans, Mice, Inbred BALB C, Trypanocidal Agents, Trypanosoma cruzi, Chagas Disease, Genome, Protozoan, Computational Biology, Mice, Metabolic Networks and Pathways, Drug Discovery, High-Throughput Screening Assays
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
