A kernel for open source drug discovery in tropical diseases

Area of Science:

• Computational biology
• Drug discovery
• Open source research

Background:

• Conventional drug development models are ineffective for diseases disproportionately affecting developing nations.
• Alternative research and development (R&D) models, including public-private partnerships and prizes, have been explored.
• Open source drug discovery, a promising avenue, has been hindered by the absence of a foundational dataset for collaborative improvement.

Purpose of the Study:

• To establish a foundational dataset, or "kernel," to facilitate open source drug discovery for tropical diseases.
• To create a resource that enables a critical mass of volunteer contributions for drug development.
• To overcome the historical barrier to open source collaboration in drug research.

Main Methods:

• Utilized a computational pipeline for comparative structure modeling of target proteins.
• Predicted ligand binding sites on protein surfaces.
• Assessed the similarity of predicted ligands to existing drugs.

Main Results:

• Developed a kernel with 143 and 297 protein targets from ten pathogen genomes.
• Identified targets predicted to bind known drugs or similar molecules.
• Experimentally validated predictions using NMR spectroscopy, confirming one target and invalidating another.

Conclusions:

• The Tropical Disease Initiative (TDI) kernel is available under a Creative Commons license.
• This resource aims to foster collaborative drug discovery for tropical diseases.
• The kernel provides potential drug targets and candidates to nucleate an online open source community.