Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]

1 table. -- ChEMBL dataset used to train and validate the model, compounds codes, SMILE codes, preclinical assay conditions, observed values, predicted classifications, probabilities, etc.

Detalles Bibliográficos
Autores: Santiago, Carlos, Ortega Tenezaca, Bernabé, Barbolla, Iratxe, Fundora, Brenda, Arrasate, Sonia, Dea-Ayuela, M. Auxiliadora, González-Díaz, Humberto, Sotomayor, Nuria, Lete, Esther
Tipo de recurso: conjunto de datos
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/331583
Acceso en línea:http://hdl.handle.net/10261/331583
Access Level:acceso abierto
Palabra clave:Potential target proteins
Friendly interface making
Relative biological activity
700 activity scores
Antileishmanial compound candidates
5bd
5bc
Obtain ifptml models
Different ml algorithms
b
50
Leishmanicidal activity
vs
Vitro
Leishmania
Antileishmanial hits
Throughput screening
Svm ),
rf ),
Random forests
Performed calculating
One strategy
Logistic regression
Large space
j774 cells
General models
Evaluated finding
Computational high
Chembl dataset
Cell lines
Approximately 6
87 μm
28 derivatives
100 μg
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network_acronym_str ES
network_name_str España
repository_id_str
spelling Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]Santiago, CarlosOrtega Tenezaca, BernabéBarbolla, IratxeFundora, BrendaArrasate, SoniaDea-Ayuela, M. AuxiliadoraGonzález-Díaz, HumbertoSotomayor, NuriaLete, EstherPotential target proteinsFriendly interface makingRelative biological activity700 activity scoresAntileishmanial compound candidates5bd5bcObtain ifptml modelsDifferent ml algorithmsb50Leishmanicidal activityvsVitroLeishmaniaAntileishmanial hitsThroughput screeningSvm ),rf ),Random forestsPerformed calculatingOne strategyLogistic regressionLarge spacej774 cellsGeneral modelsEvaluated findingComputational highChembl datasetCell linesApproximately 687 μm28 derivatives100 μg1 table. -- ChEMBL dataset used to train and validate the model, compounds codes, SMILE codes, preclinical assay conditions, observed values, predicted classifications, probabilities, etc.In this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81–98%) in training and validation series. The use of this software has been illustrated with a practical case study focused on a series of 28 derivatives of 2-acylpyrroles 5a,b, obtained through a Pd­(II)-catalyzed C–H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated finding that compounds 5bc (IC50 = 30.87 μM, SI > 10.17) and 5bd (IC50 = 16.87 μM, SI > 10.67) were approximately 6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity, CC50 > 100 μg/mL in J774 cells. Interestingly, the IFPMTL-LOGR model predicts correctly the relative biological activity of these series of acylpyrroles. A computational high-throughput screening (cHTS) study of 2-acylpyrroles 5a,b has been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. The present work also points to 2-acylpyrroles as new lead compounds worthy of further optimization as antileishmanial hits.Peer reviewedFigshareConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232022info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1application/vnd.ms-excelhttp://hdl.handle.net/10261/331583reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSantiago, Carlos; Ortega Tenezaca, Bernabé; Barbolla, Iratxe; Fundora, Brenda; Arrasate, Sonia; Dea-Ayuela, M. Auxiliadora; González-Díaz, Humberto; Sotomayor, Nuria; Lete, Esther. Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives. http://dx.doi.org/10.1021/acs.jcim.2c00731. http://hdl.handle.net/10261/304205https://doi.org/10.1021/acs.jcim.2c00731.s001Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3315832026-05-22T06:33:51Z
dc.title.none.fl_str_mv Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
title Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
spellingShingle Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
Santiago, Carlos
Potential target proteins
Friendly interface making
Relative biological activity
700 activity scores
Antileishmanial compound candidates
5bd
5bc
Obtain ifptml models
Different ml algorithms
b
50
Leishmanicidal activity
vs
Vitro
Leishmania
Antileishmanial hits
Throughput screening
Svm ),
rf ),
Random forests
Performed calculating
One strategy
Logistic regression
Large space
j774 cells
General models
Evaluated finding
Computational high
Chembl dataset
Cell lines
Approximately 6
87 μm
28 derivatives
100 μg
title_short Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
title_full Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
title_fullStr Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
title_full_unstemmed Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
title_sort Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives [Dataset]
dc.creator.none.fl_str_mv Santiago, Carlos
Ortega Tenezaca, Bernabé
Barbolla, Iratxe
Fundora, Brenda
Arrasate, Sonia
Dea-Ayuela, M. Auxiliadora
González-Díaz, Humberto
Sotomayor, Nuria
Lete, Esther
author Santiago, Carlos
author_facet Santiago, Carlos
Ortega Tenezaca, Bernabé
Barbolla, Iratxe
Fundora, Brenda
Arrasate, Sonia
Dea-Ayuela, M. Auxiliadora
González-Díaz, Humberto
Sotomayor, Nuria
Lete, Esther
author_role author
author2 Ortega Tenezaca, Bernabé
Barbolla, Iratxe
Fundora, Brenda
Arrasate, Sonia
Dea-Ayuela, M. Auxiliadora
González-Díaz, Humberto
Sotomayor, Nuria
Lete, Esther
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Potential target proteins
Friendly interface making
Relative biological activity
700 activity scores
Antileishmanial compound candidates
5bd
5bc
Obtain ifptml models
Different ml algorithms
b
50
Leishmanicidal activity
vs
Vitro
Leishmania
Antileishmanial hits
Throughput screening
Svm ),
rf ),
Random forests
Performed calculating
One strategy
Logistic regression
Large space
j774 cells
General models
Evaluated finding
Computational high
Chembl dataset
Cell lines
Approximately 6
87 μm
28 derivatives
100 μg
topic Potential target proteins
Friendly interface making
Relative biological activity
700 activity scores
Antileishmanial compound candidates
5bd
5bc
Obtain ifptml models
Different ml algorithms
b
50
Leishmanicidal activity
vs
Vitro
Leishmania
Antileishmanial hits
Throughput screening
Svm ),
rf ),
Random forests
Performed calculating
One strategy
Logistic regression
Large space
j774 cells
General models
Evaluated finding
Computational high
Chembl dataset
Cell lines
Approximately 6
87 μm
28 derivatives
100 μg
description 1 table. -- ChEMBL dataset used to train and validate the model, compounds codes, SMILE codes, preclinical assay conditions, observed values, predicted classifications, probabilities, etc.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
http://purl.org/coar/resource_type/c_ddb1
format dataset
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/331583
url http://hdl.handle.net/10261/331583
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Santiago, Carlos; Ortega Tenezaca, Bernabé; Barbolla, Iratxe; Fundora, Brenda; Arrasate, Sonia; Dea-Ayuela, M. Auxiliadora; González-Díaz, Humberto; Sotomayor, Nuria; Lete, Esther. Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives. http://dx.doi.org/10.1021/acs.jcim.2c00731. http://hdl.handle.net/10261/304205
https://doi.org/10.1021/acs.jcim.2c00731.s001
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/vnd.ms-excel
dc.publisher.none.fl_str_mv Figshare
publisher.none.fl_str_mv Figshare
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869425110016851968
score 15.81155

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