MACHINE LEARNING-ASSISTED COMPUTATIONAL CHEMISTRY FOR ACCELERATED MOLECULAR DESIGN AND SUSTAINABLE CHEMICAL INNOVATION

Abstract

Machine learning-assisted computational chemistry has emerged as a transformative approach for accelerating molecular design and promoting sustainable chemical innovation by integrating data-driven algorithms with computational modeling techniques. This review provides an overview of the principles, methodologies, and applications of machine learning in computational chemistry, highlighting its role in molecular property prediction, quantitative structure–activity and structure–property relationship modeling, virtual screening, de novo molecular design, and structure-based drug discovery. The review further discusses key molecular representation strategies, including molecular descriptors, fingerprints, SMILES, SELFIES, graph-based representations, and biomolecular features, which serve as the foundation for predictive modeling. Major machine learning algorithms, including regression models, support vector machines, random forests, artificial neural networks, graph neural networks, transformer models, and generative artificial intelligence, are examined with respect to their contributions to computational chemistry workflows. In addition, the review explores the expanding applications of machine learning in molecular biology and genetics, including drug target identification, protein structure prediction, pharmacogenomics, biomolecular interaction analysis, disease-associated molecular discovery, and precision medicine. The role of artificial intelligence in advancing green molecular design, sustainable catalyst discovery, reaction optimization, eco-friendly material development, and sustainable pharmaceutical manufacturing is also highlighted. Finally, emerging trends such as generative AI, large language models, explainable artificial intelligence, multi-omics integration, and autonomous molecular discovery platforms are discussed alongside current challenges and future perspectives. Collectively, machine learning-assisted computational chemistry represents a powerful interdisciplinary framework for accelerating molecular discovery while supporting sustainable and biologically informed chemical innovation.