Artificial Intelligence (AI) has rapidly transformed pharmaceutical sciences, biotechnology, and life science research by enabling intelligent data analysis, predictive modeling, and automation of complex scientific workflows. Modern AI technologies, including machine learning, deep learning, natural language processing, computer vision, and generative AI, have accelerated drug discovery, biomarker identification, precision medicine, protein structure prediction, clinical research, and healthcare decision-making. AI-powered computational platforms are increasingly used for virtual screening, molecular design, quantitative structure–activity relationship modeling, pharmacokinetic prediction, toxicity assessment, pharmaceutical quality assurance, and biotechnology applications such as synthetic biology and genome engineering. Breakthrough innovations including AlphaFold have significantly improved protein structure prediction and expanded opportunities for structure-based drug discovery. Despite these remarkable advances, challenges related to data quality, algorithm transparency, regulatory compliance, ethical concerns, and cybersecurity remain important barriers to broader implementation. This review summarizes current advances in AI integration across pharmaceutical, biotechnology, and life science research, discusses emerging applications and limitations, and highlights future perspectives for AI-driven scientific innovation. The successful integration of AI with multidisciplinary biomedical research has the potential to accelerate therapeutic discovery, improve healthcare outcomes, and support the development of safer, more effective, and personalized medicines.
Shailendra Sanjay Suryawanshi, Arif Salim Jamadar, Ronak Dsouza, Adarsh Anil Kamate· Zenodo (CERN European Organi...· 0 citations
Artificial Intelligence (AI) has rapidly transformed pharmaceutical sciences, biotechnology, and life science research by enabling intelligent data analysis, predictive modeling, and automation of complex scientific workflows. Modern AI technologies, including machine learning, deep learning, natural language processing, computer vision, and generative AI, have accelerated drug discovery, biomarker identification, precision medicine, protein structure prediction, clinical research, and healthcare decision-making. AI-powered computational platforms are increasingly used for virtual screening, molecular design, quantitative structure–activity relationship modeling, pharmacokinetic prediction, toxicity assessment, pharmaceutical quality assurance, and biotechnology applications such as synthetic biology and genome engineering. Breakthrough innovations including AlphaFold have significantly improved protein structure prediction and expanded opportunities for structure-based drug discovery. Despite these remarkable advances, challenges related to data quality, algorithm transparency, regulatory compliance, ethical concerns, and cybersecurity remain important barriers to broader implementation. This review summarizes current advances in AI integration across pharmaceutical, biotechnology, and life science research, discusses emerging applications and limitations, and highlights future perspectives for AI-driven scientific innovation. The successful integration of AI with multidisciplinary biomedical research has the potential to accelerate therapeutic discovery, improve healthcare outcomes, and support the development of safer, more effective, and personalized medicines.
Shailendra Sanjay Suryawanshi, Arif Salim Jamadar, Ronak Dsouza, Adarsh Anil Kamate· Zenodo (CERN European Organi...· 0 citations
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