Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

Abstract

Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).

Chemoinformatics Strategies for Leishmaniasis Drug Discovery

“…The use of SBDD and LBDD methods in leishmaniasis drug discovery is an encouraging strategy that has advanced alongside the progress made in the NTD field ( Njogu et al, 2016 ). Chemoinformatics studies have incorporated different SBDD workflows that focus on established and newly discovered molecular targets.…”

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

“…Computational tools have been increasingly used in the area of TB research. A recent review by Chibale and colleagues [41] described the extensive structure-based and ligand-based approaches used for TB, malaria and trypanosomal disease research, however they did not specifically address machine learning applications. Machine learning techniques have been applied most extensively for genetics and genomics [42] as well as applied to antibacterial drug discovery [43].…”

Chirality‐Induced Variation in Interaction of Two Similar Copper(II) Coordination Polymers with Calf Thymus DNA: Exploration of Their Antimicrobial Activity and Cytotoxicity

“…Copper(II) complexes with organic ligands are also reported to be effective in DNA binding and active against cancer cells . Molecular docking algorithms has emerged as an alternative method to facilitate drug discovery via virtual screening of metal complexes with various viral proliferating and cancer causing proteins ,. Molecular docking determines specific interactions between synthetic and biological polymers, therefore, it is an interesting platform for advancements in modern medicine and pharmacy ,…”