The Role of Artificial Intelligence in Accelerating Drug Discovery and Biomedical Research
Keywords:
Artificial intelligence, drug discovery, biomedical research, machine learning, molecular modeling, clinical trials, predictive analytics, precision medicineAbstract
Purpose
This paper investigates the transformative impact of artificial intelligence (AI) on drug discovery and biomedical research, emphasizing advancements in automation, molecular modeling, and biomarker identification. It aims to assess how AI technologies have redefined the timelines, cost structures, and success rates of early-phase research and clinical trials.
Design/methodology/approach
This study employs a narrative review method, synthesizing recent interdisciplinary literature, patent filings, and public-private research initiatives. Comparative analysis is conducted across various AI-driven platforms, highlighting their implementation in pharmaceutical pipelines and precision medicine.
Findings
AI has significantly reduced the average time required for early-stage drug candidate identification. Integration of large biomedical datasets with machine learning models has enhanced the predictive power in target validation, toxicity screening, and patient stratification. AI has emerged not only as a computational tool but also as a decision-support system in translational medicine.
Practical implications
Pharmaceutical companies and research institutions can leverage AI to de-risk clinical trials and improve the selection of viable drug candidates. Regulatory bodies must adapt to AI-integrated pipelines, and workforce training is needed to bridge computational and biomedical domains.
Originality/value
This paper offers an up-to-date synthesis of AI’s role in biomedical innovation, proposing a framework for its ethical and effective deployment across the drug development lifecycle.
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