研究紹介
研究テーマ
Artificial Intelligence driven optimization of chemical reaction conditions
キーワード
Chemical databases, Quantitative Structure-Property Relationships (QSPR), Machine learning, Active learning
研究概要
My research activities lie in the use of chemoinformatics tools to better understand and predict chemical properties and mecanisms. I have been working on the design of predictive models for the prediction of eco-toxicological properties such as the ready biodegradability. During my PhD thesis, I focused on the virtual generation of new chemical compounds possessing desired properties (inverse-QSPR). I implemented and improved currents inverse-QSPR methods and proposed a set of new metrics to evaluate the performance of these methods. My current reseach focuses on the use of Active Learning to optimize reaction conditions.
代表的な研究成果
- Comparisons of Molecular Structure Generation Methods Based on Fragment Assemblies and Genetic Graphs
P. Gantzer, B. Creton, C. Nieto‐Draghi. J. Chem. Inf. Model., 2021, 61, 4245-4258.
DOI: 10.1021/acs.jcim.1c00803 - Inverse‐QSPR for de novo Design: A Review
P. Gantzer, B. Creton, C. Nieto‐Draghi. Mol. Inf., 2020, 39, 1900087.
DOI: 10.1002/minf.201900087 - Modelling of ready biodegradability based on combined public and industrial data sources
F. Lunghini, G. Marcou, P. Gantzer, P. Azam, D. Horvath, E. Van Miert, A. Varnek. Environmental Research, 2019, 31, 1-16.
DOI: 10.1080/1062936X.2019.1697360