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Curr Med Chem. 2019;26(23):4355-4379. doi: 10.2174/0929867325666180309114824.

In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases.

Author information

1
LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil.
2
Laboratory of Cheminformatics, Centro Universitario de Anapolis (UniEVANGELICA), Anapolis, GO, 75083-515, Brazil.
3
GenoBio - Laboratory of Genomics and Biotechnology, Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, GO, 74605- 220, Brazil.
4
Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, 1349-008, Portugal.

Abstract

Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safe and effective treatments. In such context, drug repositioning, which allows finding novel indications for approved drugs whose pharmacokinetic and safety profiles are already known, emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent of the typical drug discovery process that involves the systematic screening of chemical compounds against drug targets in high-throughput screening (HTS) efforts, for the identification of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics attempts to identify all potential ligands for all possible targets and diseases. In this review, we summarize current methodological development efforts in drug repositioning that use state-of-the-art computational ligand- and structure-based chemogenomics approaches. Furthermore, we highlighted the recent progress in computational drug repositioning for some NTDs, based on curation and modeling of genomic, biological, and chemical data. Additionally, we also present in-house and other successful examples and suggest possible solutions to existing pitfalls.

KEYWORDS:

Neglected tropical diseases; chemogenomics; docking; drug repositioning; machine learning; pharmacophores; protein alignment; similarity search.

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