High Throughput and Computational Repurposing for Neglected Diseases.

Area of Science:

• Drug discovery and development
• Medicinal chemistry
• Computational biology

Background:

• Neglected tropical diseases (NTDs) affect over 1.4 billion people in impoverished regions.
• These 23 WHO-prioritized diseases stem from diverse pathogens and largely lack effective treatments.
• NTDs impose significant economic burdens on developing nations.

Purpose of the Study:

• To review high-throughput screening and computational approaches for NTD drug discovery.
• To collate and analyze molecules identified through repurposing efforts.
• To highlight the need for novel therapeutics against NTDs.

Main Methods:

• High-throughput screening (HTS) of compound libraries.
• Computational drug repurposing strategies.
• Molecular property calculations for identified compounds.
• Utilization of public domain datasets for machine learning model construction.

Main Results:

• Global drug repurposing initiatives have identified promising drug candidates for NTDs.
• Identified compounds generally exhibit favorable drug-like properties.
• Availability of large datasets facilitates machine learning-driven drug discovery.

Conclusions:

• Recent experimental and computational repurposing screens have yielded compounds, often existing drugs.
• Drug repurposing offers a potentially more efficient route to develop treatments for NTDs.
• These strategies could be beneficial for other neglected diseases, addressing market viability concerns for pharmaceutical companies.