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dc.date.available
2026-03-10T11:05:01Z  
dc.identifier.citation
Urán Landaburu, Héctor Lionel; Didier Garnham, Mercedes Monica; Salas Sarduy, Emir; Agüero, Fernan Gonzalo; (2026): Prioritization of chemical scaffolds using the TDR Targets database: An integrative workflow for Trypanosoma cruzi drug discovery. Consejo Nacional de Investigaciones Científicas y Técnicas. (dataset). http://hdl.handle.net/11336/282756  
dc.identifier.uri
http://hdl.handle.net/11336/282756  
dc.description.abstract
Prioritization of chemical scaffolds using the TDR Targets database: an integrative workflow for Trypanosoma cruzi drug discovery Chagas disease, caused by the parasite Trypanosoma cruzi, faces a critical innovation gap in drug development, with current treatments hindered by toxicity and limited efficacy. To address this, we implemented an integrative chemogenomic workflow using the TDR Targets database to prioritize drug candidates. To prioritize repurposing candidates for T. cruzi, we designed a query to retrieve compounds active against validated targets in other organisms, provided an orthologous gene exists in T. cruzi and the compound has no recorded activity against trypanosomatids and their associations predicted by the TDR Targets multilayer network. On those associations we applied sequential filters based on metabolic relevance, and commercial availability via the MolPort API obtaining a focused set of 378 high-priority compounds. A central feature of this workflow was the partitioning of these compounds into 16 distinct chemical libraries, each defined by unique scaffolds such as benzamidines, sulfonamides, and azoles. For experimental validation, we manually curated two of these libraries, containing piperazine and nitro derivatives. From the 21 compounds acquired for in vitro testing against T. cruzi in intracellular models of infection, 7 demonstrated selective trypanocidal activity, with two lead hits achieving submicromolar EC50 values. Crucially, while our experimental focus was on these two series, the remaining 14 curated libraries, representing a broad range of chemical space and putative target associations, which are fully available for public exploration and further biological assaying. These results demonstrate the efficiency of our prioritization pipeline and provide the scientific community with a pre-filtered, commercially accessible resource to accelerate the discovery of new leads for Chagas disease.  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.title
Prioritization of chemical scaffolds using the TDR Targets database: An integrative workflow for Trypanosoma cruzi drug discovery  
dc.type
dataset  
dc.date.updated
2026-03-10T10:08:27Z  
dc.description.fil
Fil: Urán Landaburu, Héctor Lionel. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina  
dc.description.fil
Fil: Didier Garnham, Mercedes Monica. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina  
dc.description.fil
Fil: Salas Sarduy, Emir. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina  
dc.description.fil
Fil: Agüero, Fernan Gonzalo. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina  
dc.datacite.PublicationYear
2026  
dc.datacite.Creator
Urán Landaburu, Héctor Lionel  
dc.datacite.Creator
Didier Garnham, Mercedes Monica  
dc.datacite.Creator
Salas Sarduy, Emir  
dc.datacite.Creator
Agüero, Fernan Gonzalo  
dc.datacite.affiliation
Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas  
dc.datacite.affiliation
Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas  
dc.datacite.affiliation
Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas  
dc.datacite.affiliation
Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas  
dc.datacite.publisher
Consejo Nacional de Investigaciones Científicas y Técnicas  
dc.datacite.subject
Bioquímica y Biología Molecular  
dc.datacite.subject
Ciencias Biológicas  
dc.datacite.subject
CIENCIAS NATURALES Y EXACTAS  
dc.datacite.date
2021/2023  
dc.datacite.DateType
Creado  
dc.datacite.language
eng  
dc.datacite.AlternateIdentifierType
info:eu-repo/semantics/altIdentifier/url/https://github.com/trypanosomatics/TDR-screening/  
dc.datacite.version
1.0  
dc.datacite.description
# Description of the data and file structure ## Prioritization and Screening of compounds using the TDR Targets Database The materials in this repository are data, code, and research outputs that form part of the supporting materials for the paper: "Prioritization of chemical scaffolds using the TDR Targets database: an integrative workflow for Trypanosoma cruzi drug discovery" by Lionel Urán Landaburu, Mercedes Didier Garnham, Emir Salas-Sarduy and Fernán Agüero. ## Contents data -- datasets used in this work code -- interactive Jupyter Notebook (Python) network-based-library -- chemical libraries produced (research outputs) ### Files and variables: File: commercial-compounds-molport.csv Format: TXT, comma-separated-values Description: Molport: All commercially available drugs (at the time of searching) Variables: Query String: SMILES of query Search Type: EXACT Search Result: search outcome Search Result Message: message provided by Molport API Canonical SMILES: SMILES of subject (Molport hit) MolPort ID: Identifier of the molecule at Molport Verified amount (mg): literal  Unverified amount (mg): literal  Link: link to Molport website  File: structures.sdf Format: TXT, Structure-data file (chemical) Description: Drugbank: all approved and withdrawn drugs File: tcr_ndp_gdi.tsv.gz Format: GZIP, compressed TXT file (comma-separated-values) Description: TDR Targets: All putative gene-drug interactions for T. cruzi Variables: mol_id: unique identifier for chemicals smiles: SMILES lexicographic representation for chemicals inchikey: standard unique hash of the INCHI identifier for chemicals gene_name: unique identifier for genes / targets gene_product: description (annotation) of genes / targets File: tested-tryps.smiles Description: TDR Targets: All drugs tested (active and inactive) against trypanosomes (Trypanosoma spp, Leishmania spp) File: tcr_cherrypicked_routes.csv Description: TDR Targets: Targets matching pathways for carbon and amminoacid metabolism. Variables: gene_name: accession, identifier gene_product: gene product description (annotation) Weight: TDR Targets prioritization cumulative weight Query 5 Carbo (weight): weight assigned to this query Query 4 Lipids (weight): weight assigned to this query Query 6 aminoacids (weight): weight assigned to this query File: libraryPreparation.ipynb Description: Code (Jupyter Python Notebook)  File: libraryAgg_adenine.html Description: Adenine sublibrary File: libraryAgg_benzamidine.html Description: Benzamidine sublibrary File: libraryAgg_chromenes.html Description: Chromene sublibrary File: libraryAgg_furan.html Description: Furan sublibrary File: libraryAgg_azole.html Description: Azole sublibrary File: libraryAgg_indole.html Description: Indole sublibrary File: libraryAgg_morpholine.html Description: Morpholine sublibrary File: libraryAgg_benzothiazole.html Description: Benzothiazole sublibrary File: libraryAgg_picolinamide.html Description: Picolinamide sublibrary File: libraryAgg_oxazole.html Description: Oxazole sublibrary File: libraryAgg_resto.html Description: Other sublibrary File: libraryAgg_nitro.html Description: Nitro sublibrary File: libraryAgg_piperazine.html Description: Piperazine sublibrary File: libraryAgg_pirrolone.html Description: Pirrolone sublibrary File: libraryAgg_sulfonamide.html Description: Sulfonamide sublibrary File: libraryAgg_thiazoleidine.html Description: Thiazoleidine sublibrary File: libraryAgg_thiazole.html Description: Thiazole sublibrary File: libraryAgg_napthalene_diimide.html Description: Naphtalene diimide sublibrary File: libraryAgg_waltherione.html Description: Waltherione sublibrary Code/software: Jupyter Python Notebook used to prepare chemical libraries  Dependencies: pandas, numpy, rdkit, matplotlib, seaborn, multiprocessing, re, requests, os, pickle, glob, random, base64, io ## Access information Other publicly accessible locations of the data: https://github.com/trypanosomatics/TDR-screening/ (except gene-drug-interaction data due to size restrictions) ## Data was derived from the following sources: - TDR Targets database (https://tdrtargets.org) - ChEMBL (https://www.ebi.ac.uk/chembl/) - Molport (https://www.molport.com/)  
dc.datacite.DescriptionType
Información Técnica  
dc.datacite.FundingReference
00013-2019-PICT  
dc.datacite.FunderName
Ministerio de Ciencia. Tecnología e Innovación Productiva. Agencia Nacional de Promoción Científica y Tecnológica  
dc.subject.keyword
Drug discovery  
dc.subject.keyword
Enfermedad de chagas  
dc.subject.keyword
Chagas disease  
dc.subject.keyword
Trypanosoma cruzi  
dc.subject.keyword
TDR Targets  
dc.subject.keyword
Chemical libraries  
dc.datacite.resourceTypeGeneral
dataset  
dc.conicet.datoinvestigacionid
31862  
dc.datacite.awardTitle
Identificación y reposicionamiento de compuestos bioactivos para el tratamiento de la Enfermedad de Chagas  
dc.conicet.justificacion
Son datos integrados, extraidos de genomas de referencia ya secuenciados y datos de fármacos y sus actividades provenientes de la literatura.  
dc.datacite.formatedDate
2021-2023