Author

Luís Fernando Saraiva Macedo Timmers

UnivatesORCID 0000-0003-0399-5376Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Alexia de Matos CzeczotiD· 1 shared Ana Micaela Camini· 1 shared Aureo André KarolczakiD· 1 shared Cristiano Valim BizarroiD· 1 shared David ShumiD· 1 shared Jinyeong HeoiD· 1 shared Kyu-Ho Paul ParkiD· 1 shared Luiz Augusto BassoiD· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBiochemistry

Alexia de Matos CzeczotORCID

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2 papers

Recent·Most cited

BiochemistryFrontiers in Tropical DiseasesPeer-reviewedJun 13, 2024

Identification and characterization of new structural scaffolds modulating the activity of Mycobacterium tuberculosis dihydroneopterin aldolase (FolB) in vitro

Virgínia Carla de Almeida Falcão, Alexia de Matos Czeczot, Mohammad Maqusood Alam…·Vincent Delorme

The paper identifies and characterizes new structural scaffolds that modulate the activity of Mycobacterium tuberculosis dihydroneopterin aldolase (FolB) in vitro, finding compounds with IC50 values ranging from 2.6 to 47 μM.

1 citation

experimental assaydrug response / drug discovery

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BioinformaticsbioRxivPreprintMar 13, 2023

Developing QSAR Models for the Identification of Inhibitors Targeting Mycobacterium tuberculosis Enoyl-ACP Reductase Enzyme

Aureo André Karolczak, Luís Fernando Saraiva Macedo Timmers, Rafael Andrade Caceres

The paper develops QSAR models to identify inhibitors of Mycobacterium tuberculosis InhA enzyme, finding ten molecules with high predicted activity and favorable binding energy.

DOI 10.1101/2023.03.13.532463

statistical modelmachine learning (classical)

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