Python in Pharmaceutical Industries and Research: Applications, Opportunities and Future Perspectives
Main Article Content
Abstract
The pharmaceutical industry is undergoing rapid digital transformation driven by advances in artificial intelligence (AI), machine learning (ML), big data analytics, and computational modelling. Among modern programming languages, Python has emerged as one of the most versatile and widely adopted tools in pharmaceutical sciences because of its simplicity, open-source nature, extensive scientific libraries, and strong community support. Its applications span cheminformatics, bioinformatics, molecular modelling, quantitative structure–activity relationship (QSAR) analysis, pharmacokinetics, pharmacodynamics, clinical trial data management, pharmacovigilance, manufacturing process optimization, and regulatory data analysis. Libraries such as NumPy, Pandas, SciPy, Matplotlib, Scikit-learn, TensorFlow, PyTorch, RDKit, and Biopython have significantly expanded the analytical capabilities available to pharmaceutical scientists. The integration of Python with cloud computing, high-performance computing, and generative AI is further transforming pharmaceutical research by reducing development timelines and improving decision-making. Python also supports reproducible research, automation of routine analytical tasks, and visualization of complex scientific data, making it an indispensable tool in both academia and industry. This review discusses the role of Python across the pharmaceutical product lifecycle, highlights major scientific libraries and industrial applications, examines current challenges, and explores future opportunities for integrating Python with emerging technologies to enhance pharmaceutical innovation.
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