Nurkin, MirasKuatbekov, DiasAkhmetzhanov, Kuanysh2024-06-182024-06-182024-04-20Nurkin, M., Kuatbekov, D., & Akhmetzhanov, K. (2024). ML-based Prediction and Generation of Fluorescent Molecules and Their Properties. Nazarbayev University School of Engineering and Digital Scienceshttp://nur.nu.edu.kz/handle/123456789/7885This report provides an in-depth overview of the “ML-based Prediction and Generation of Fluorescence Molecules and Their Properties”, which dives into the usage of machine learning techniques to accelerate the discovery process of new fluorescence molecules and predict their properties efficiently. This project aims to reduce the time and costs associated with discovery and property prediction of organic compounds. A key component of our work was the use of generative models, including Transmol and Chemical Language Model and the usage of deep learning architectures for property prediction. The results of our approaches have been highly promising. We achieved nearly state-of-art performance in predicting molecular properties and constructed a database of newly sampled molecules. It is worth mentioning that with ensemble learning techniques, we attained an 𝑅 score of 0.956 for absorption max, 2 0.901 for emission max, and 0.73 for quantum yield. It demonstrates the high performance of our models. During working on this project, we have learned different domains, such as RDKit for cheminformatics, various molecular representations, and the integration of generative models with classical machine learning algorithms to improve property prediction and synthesis. Finally, we have developed a database containing newly sampled molecules and with their properties, as predicted by our best performing modelsenAttribution-NonCommercial-NoDerivs 3.0 United StatesType of access: Open AccessMolecular Property PredictionFluorescent MoleculesBachelor's thesis, capstone project