In silico drug discovery approaches on grid computing infrastructures.

Area of Science:

• Computational chemistry
• Bioinformatics
• eScience

Background:

• Drug discovery relies on screening compound databases against protein targets.
• In silico methods like virtual screening are crucial but computationally intensive.
• Growing databases and screening approaches necessitate increased computational power and complex workflows.

Purpose of the Study:

• To review the application of Grid computing in in silico drug discovery.
• To explore the potential of Grid infrastructures for handling large-scale computational demands.
• To envision future integrated workflows for drug discovery on Grids.

Main Methods:

• Utilizing Grid computing infrastructures for high computational and data storage demands.
• Leveraging eScience paradigms for collaborative, data- and compute-intensive applications.
• Mobilizing shared computing resources, data collections, and analysis services on demand.

Main Results:

• Grid computing effectively addresses the computational and storage needs of large-scale in silico drug discovery.
• Pharmaceutical industry and academic research have successfully employed Grid infrastructures.
• Grid environments facilitate seamless access to substantial computing resources and data.

Conclusions:

• Grid computing is a vital infrastructure for modern in silico drug discovery.
• Future developments will focus on more complex and integrated workflows on Grids.
• Grids enable efficient target identification, validation, screening, and data mining.