Design of efficient computational workflows for in silico drug repurposing

Area of Science:

• Computational biology
• Pharmacology
• Bioinformatics

Background:

• Drug repurposing accelerates therapeutic development by identifying new uses for existing drugs.
• In silico methods are crucial for efficient drug repurposing, but require careful consideration of technological trends and data.
• Understanding algorithm characteristics is key to successful computational drug repurposing.

Purpose of the Study:

• To provide a comprehensive overview of current in silico drug repurposing methods.
• To link technological trends, data availability, and algorithm characteristics in drug repurposing.
• To propose a generic modular organization for a drug repurposing workflow.

Main Methods:

• Review of 3D structure-based, similarity-based, inference-based, and machine learning (ML)-based methods.
• Analysis of advantages and disadvantages of various computational repurposing approaches.
• Case study: Computational repurposing of fasudil as an autophagy enhancer.

Main Results:

• Identification of three key technical challenges in current in silico drug repurposing.
• Demonstration of a modular repurposing workflow using fasudil.
• Summary of the strengths and weaknesses of different computational methods.

Conclusions:

• In silico drug repurposing offers significant potential for accelerating drug discovery.
• Future research directions include leveraging advanced methods like deep learning for enhanced drug repurposing.
• A modular workflow approach can standardize and improve the efficiency of computational drug repurposing efforts.