• Dr. Koyel Misra
• Dr. Subhajit Mukherjee
• Dr. Bipasha Mridha Ghosh
• Dr. Sanchita Sarkar

The understanding of molecular interactions, drug design, and chemical synthesis has undergone a paradigm shift due to the multidisciplinary integration of chemistry and informatics in modern scientific discovery. This study provides a comprehensive examination of the significant advancements in chemoinformatics enabled by the transformative potential of machine learning techniques. The study begins by explaining the fundamental concepts and principles of chemoinformatics to provide a solid understanding of its crucial role in exploring the chemical universe. It then delves into the emerging field of machine learning, highlighting its applications and adaptations for chemical studies. Machine learning enhances chemoinformatics by enabling predictive modeling, pattern detection, and knowledge extraction from vast chemical datasets. Additionally, the study meticulously lists and evaluates various machine learning techniques used in chemoinformatics, such as molecular property prediction, drug discovery, virtual screening, and molecular generative models.

Chemoinformatics, Machine Learning, Molecular Sciences, Drug Discovery, Data-driven Chemistry, Big Data in Chemistry

"Progress in Chemoinformatics through the Application of Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.e460-e466, July-2024, Available :http://www.jetir.org/papers/JETIR2407453.pdf

"Progress in Chemoinformatics through the Application of Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppe460-e466, July-2024, Available at : http://www.jetir.org/papers/JETIR2407453.pdf