Data Driven Organic Chemistry: Role of Artificial Intelligence in Molecular Discovery

Abstract

Organic chemistry has been transformed by artificial intelligence (AI) that has facilitated the use of data in discovering molecules, predicting reactions, and optimizing syntheses. Conventional methods which depend on human intuition and trial and error tests are usually time consuming and narrow-minded. The use of AI with large chemical datasets to predict properties of molecules, create new compounds, optimize reactions, and plan synthetic pathways through machine learning, deep learning, and generative models is efficient. It is used in drug discovery, catalysis, materials science, and sustainable chemistry, which lessens the amount of work required in experiments, increases precision and efficiency. Although AI has some challenges including the quality of data, interpretation, and generalization, it supplements human knowledge, providing a breakthrough method to rapid, rational, and sustainable chemical innovation.