ガンツァー・フィリップ

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ガンツァー・フィリップ
博士研究員
List プラットフォーム（Sidorov グループ）
連絡先

p.gantzer atmark icredd.hokudai.ac.jp

SIDOROV, Pavel グループ
ジュニアPI
博士研究員

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

関連する研究記事

業績一覧

2023年

  • Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson's Catalyst Case
    R. Staub, P. Gantzer, Y. Harabuchi, S. Maeda, A. Varnek, Molecules, 2023, 28 (11), 4477
    DOI: 10.3390/molecules28114477