Literature DB >> 30378482

Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents.

Amit Kumar Halder1, M Natália Dias Soeiro Cordeiro1.   

Abstract

Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process. In the current review, we discuss recent advances pertaining to in silico analyses towards lead identification, lead modification and target identification of antileishmaniasis and anti-trypanosomiasis agents. We describe recent applications of some important in silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c) recent applications and advances in the last five years; (d) experimental validations of in silico predictions; (e) virtual screening tools; and (f) rationale or justification for the selection of these in silico methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  2D-/3D-Quantitative Structure-Activity Relationshipszzm321990QSAR; Drug design; Leishmaniasis; Machine learning tools; Trypanosomiasis; Web-platforms; virtual screening.

Mesh:

Year:  2020        PMID: 30378482     DOI: 10.2174/0929867325666181031093702

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  3 in total

Review 1.  Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.

Authors:  David A Winkler
Journal:  Front Chem       Date:  2021-03-15       Impact factor: 5.221

2.  Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis.

Authors:  Jeysson Sánchez-Suárez; Freddy A Bernal; Ericsson Coy-Barrera
Journal:  Molecules       Date:  2020-12-03       Impact factor: 4.411

Review 3.  Development of Novel Anti-Leishmanials: The Case for Structure-Based Approaches.

Authors:  Mohini Soni; J Venkatesh Pratap
Journal:  Pathogens       Date:  2022-08-22
  3 in total

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