Pharmaceutical science is entering a new era in which data can reveal patterns, predict outcomes, and guide decisions that conventional analysis may overlook. Introduction to Machine Learning in Pharmaceutical Sciences provides a clear and practical pathway into this rapidly evolving field, connecting computational principles with real pharmaceutical challenges.Beginning with the foundations of artificial intelligence, machine learning, and data science, the book explains pharmaceutical datasets, data preparation, regression, classification, clustering, decision trees, and model evaluation in an accessible yet scientifically rigorous manner. Application-focused discussions demonstrate how these methods support drug discovery, formulation development, quality control, manufacturing, pharmacovigilance, and clinical decision-making.Designed for pharmacy students, teachers, researchers, and early-career pharmaceutical professionals, the book bridges the gap between theoretical algorithms and their responsible use in pharmaceutical research and healthcare. Readers gain the conceptual foundation needed to interpret models critically, assess the reliability of predictions, and convert complex data into meaningful scientific insight.For anyone seeking to understand how machine learning is reshaping pharmaceutical sciences, this book offers an authoritative and accessible starting point.
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