Leveraging Big Data to Transform Drug Discovery.

Area of Science:

• Computational biology
• Pharmacology
• Bioinformatics

Background:

• Increasing availability of public disease and drug-related data.
• Growing application of computational methodologies in drug discovery.
• Need for structured resources and workflows for in silico drug discovery.

Purpose of the Study:

• To outline resources and tools for computational drug discovery.
• To detail in silico workflows for identifying novel drug indications.
• To discuss caveats and considerations for rigorous computational experiments.

Main Methods:

• Review and curation of existing computational resources and tools.
• In-depth analysis of two case studies employing in silico workflows.
• Identification of potential novel indications for existing drugs.

Main Results:

• Detailed description of available computational resources and tools.
• Exemplification of successful in silico workflows in drug repurposing.
• Identification of key considerations and potential pitfalls in computational drug discovery.

Conclusions:

• Computational approaches, supported by data resources and defined workflows, can effectively identify novel drug indications.
• Understanding the nuances and limitations of these methods is crucial for successful and rigorous in silico drug discovery.
• This work provides a guide for researchers to conduct their own computational drug discovery experiments.