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Discovery of new chromogenic probes for toxic using Chemistry-Trained Machine Learning


Thesis topic details

General information

Organisation

The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.

Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.

The CEA is established in ten centers spread throughout France
  

Reference

SL-DES-24-0849  

Thesis topic details

Category

Engineering science

Thesis topics

Discovery of new chromogenic probes for toxic using Chemistry-Trained Machine Learning

Contract

Thèse

Job description

Today national and international situation justify new researches on the colorimetric detection of toxic and polluting gases (referred to as analytes in the following). For the already known and studied compounds, improvement of the detection capabilities involves increasing contrast and selectivity. For potential new analytes, it is also important to prepare for rapid identification of specific chromogenic probes. The objectives of the thesis will be to discover new chromogenic probes by using computational chemistry.
First stage of the thesis: Training of the model (ML/AI) on available database. This part of the thesis will focus on establishing a precise and robust model to classify the large experimental database available from our laboratory's previous work. This involves correlating the colorimetric results with the structures and chemical properties of the molecules described by state-of-the-art methods (e.g., https://pubs.acs.org/doi/10.1021/acs.chemrev.1c00107). At the end of this learning process, we will have a predictor (SVM, LCA, PCA…) validated on our data.
Second stage: Use of the predictor model to screen in silico several hundred thousand candidate probe molecules from commercial chemical libraries (and others), correlated with their chemical structure and property descriptions as in the first stage. After this initial screening, DFT prediction of the chromogenic response will be used to refine the selection of the best candidate molecules.
Third stage: Definition and implementation of an experimental chemical testing campaign. A fast organic synthesis platform HTE (high throughput experimentation) based on the miniaturization and parallelization of chemical reactions to optimize the implementation of synthesis reactions and tests, will save considerable time, while significantly increasing the number of possible combinations. HTE also enables the synthesis of libraries of analogous compounds. Following these massive tests, a second version of the predictor will be trained and will lead to the discovery of a new generation of chromogenic molecules.

University / doctoral school

Ingénierie - Matériaux - Environnement - Energétique - Procédés - Production (IMEP2)
Université Grenoble Alpes

Thesis topic location

Site

Grenoble

Requester

Position start date

01/10/2024

Person to be contacted by the applicant

PENLOU Sébastien sebastien.penlou@cea.fr
CEA
DES/DTNM//LMSE
17 rue des martyrs
F-38054 Grenoble cedex 9
0438784636

Tutor / Responsible thesis director

LEBAIGUE Olivier olivier.lebaigue@cea.fr
CEA
DES/DTNM//LMSE
CEA/Grenoble
+33(0)4.38.78.36.70

En savoir plus


https://liten.cea.fr/cea-tech/liten