A brief survey on computational approaches to reveal drug and disease associations

Area of Science:

• Pharmacology and Pathology
• Computational Biology
• Genomic Medicine

Background:

• Complex disease phenotypes, particularly cancer, arise from multifactorial alterations.
• Current drug therapies are limited by incomplete knowledge of drug-disease associations.
• Advancements in experimental data necessitate systematic analysis for therapeutic improvement.

Purpose of the Study:

• To review computational methods for elucidating drug-disease associations.
• To highlight the role of computational approaches in pharmacology and pathology.
• To discuss the potential of advanced methods in drug discovery and development.

Main Methods:

• Systematic integration and analysis of experimental data.
• Application of computational methods to identify pharmacology-pathology relationships.
• Review of existing literature on computational drug-disease association studies.

Main Results:

• Computational methods offer insights into the complex interplay between drugs and diseases.
• These methods facilitate the understanding of molecular targets and therapeutic effects.
• The review demonstrates the utility of computational approaches in pharmacology.

Conclusions:

• Computational methods are crucial for understanding drug-disease relationships.
• Future computational techniques will leverage multi-dimensional data for enhanced drug efficacy and safety.
• This approach promises to improve existing therapies and accelerate the development of new treatments for complex diseases.