數據驅動已成為現代醫藥領域中一項關鍵的轉型力量。傳統上，新藥的研發是一個漫長且昂貴的過程，需要大量的實驗和臨床試驗。然而，隨著數據科學和人工智能的迅速發展，數據驅動的方法正在顛覆傳統的研發模式，加速了新藥研發的速度和效率。數據驅動的新藥研發轉型主要依賴於數據的收集、整合和分析。利用大數據技術，從各種來源獲取關於化學資料庫、疾病機制、基因體學、蛋白體學和生理學等方面的數據。這些數據可以通過機器學習和人工智能的方法進行分析，從而揭示複雜的關聯性和模式。這種數據驅動的方法不僅可以幫助科學家更好地理解疾病的本質，還可以發現新的藥物靶點和治療方法。

Data-driven approaches have become a critical method in the drug discovery field. Traditionally, drug development has been a lengthy and costly process that requires huge and extensive experimentation and clinical trials. However, with the rapid advancements in data science and artificial intelligence, datadriven methods are changing the traditional research and development method, accelerating the life cycle of new drug development. The transformation of data-driven drug discovery relies primarily on the collection, integration, and analysis of data. Using big data technologies, data is gathered from various sources, including chemical databases, disease mechanisms, genomics, proteomics, and physiology. This data can be analyzed using machine learning and artificial intelligence techniques, revealing complex relationships and patterns. Such data-driven approaches not only help scientists gain a better understanding of the nature of diseases but also aid in the discovery of new drug targets and treatment methods.