Author(s): Dr. Abhinav Dwivedi

Abstract: The integration of data science with physical organic chemistry represents a transformative shift from intuition-driven experimentation to predictive, data-driven chemical design. Physical organic chemistry focuses on understanding reaction mechanisms, structure–reactivity relationships, and transition states, while data science introduces computational tools such as machine learning (ML), statistical modeling, and big data analytics. This paper explores how data-driven methodologies enhance mechanistic understanding, optimize reactions, and enable predictive catalyst design. Key applications include quantitative structure–activity relationships (QSAR), reaction optimization, transition state modeling, and automated synthesis planning. The synergy between these disciplines provides a feedback loop where experimental data informs models, and models guide experiments, accelerating innovation in chemical sciences.

Keywords: Data Science, Physical Organic Chemistry, Machine Learning, QSAR, Catalysis, Reaction Mechanism, Predictive Modeling.

DOI:10.61165/sk.publisher.v10i11.5

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Application of Data Science Techniques in Physical Organic Chemistry for Advancing Predictive Molecular Modeling

Pages:52-57