Recent Advances in In Silico Target Fishing

Area of Science:

• Computational chemistry
• Drug discovery and development
• Bioinformatics

Background:

• In silico target fishing identifies potential protein targets for molecules.
• It aids in understanding mechanisms of action and biological activities.
• It is crucial for identifying off-targets and preventing adverse drug effects.

Purpose of the Study:

• To review current in silico target fishing strategies.
• To highlight ligand-based and receptor-based approaches.
• To discuss the role of machine learning and available web tools.

Main Methods:

• Review of recent in silico target fishing strategies.
• Analysis of ligand-based and receptor-based computational methods.
• Examination of machine learning applications in target fishing.

Main Results:

• In silico target fishing offers a faster, cheaper, and more efficient alternative to experimental methods.
• It is increasingly used for polypharmacology, drug repurposing, and novel target identification.
• Machine learning integration is enhancing target fishing capabilities.

Conclusions:

• In silico target fishing is a valuable and evolving tool in modern drug discovery.
• Accessible web tools and machine learning are making this approach more powerful.
• This review provides an overview of key strategies and resources for researchers.