Exploring the Benefits of Computational Biology Approaches in Identifying New Drug Targets for Severe Disorders.

Identification and validation of drug targets remains a challenge in drug discovery. It is one of the primary causes of high failure rates of therapeutics and increasing drug development costs. Computational biology has emerged as an approach to address these challenges by developing in silico models to understand the underlying disease pathophysiology. Herein, we provide a comprehensive review of contemporary computational approaches that include integrative bioinformatics, network pharmacology, systems biology, and machine learning to aid target identification. We analyse and report the successes of these approaches for several complex diseases such as neurological disorders (Alzheimer's disease, Parkinson's disease, Huntington's disease), autoimmune disorders (Rheumatoid arthritis, Type 1 diabetes, Systemic lupus erythematosus), and metabolic disorders (Type 2 Diabetes, COPD, Coronary artery disease). For each disease, we provide representative examples highlighting the use of computational strategies to interrogate high-throughput omics datasets to identify key molecular drivers, regulatory networks, and druggable targets. This review highlights the computational methods accelerating the translation of large-scale multi-omics data into validated drug targets. Ultimately, these methods improve the precision and success of drug development for complex human diseases.

Computational Biology, Target Identification, Drug Discovery, Machine Learning, Integrative Bioinformatics, Complex Diseases

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