CIS - "Get to know your neighbors" Seminar series - Prof. Philippe Schwaller
Event details
| Date | 05.09.2022 |
| Hour | 15:15 › 16:15 |
| Speaker | Prof. Philippe Schwaller |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Title: Accelerating Chemical Synthesis with Transformers
Abstract: In organic chemistry, we are currently witnessing a rise in artificial intelligence (AI) approaches, which show great potential for improving molecular designs, facilitating synthesis, and accelerating the discovery of novel molecules. Based on an analogy between written language and organic chemistry, we built linguistics-inspired Transformer models for chemical reaction prediction [1, 2], synthesis planning [3], and the prediction of experimental actions [4,5]. We extended the models to chemical reaction classification and fingerprints [6]. By finding a mapping from discrete reactions to continuous vectors, we enabled efficient chemical reaction space exploration. Intrigued by the remarkable performance of chemical language models, we discovered that the models can capture how atoms rearrange during a reaction, without supervision or human labelling, leading to the development of the open-source atom-mapping tool RXNMapper (http://rxnmapper.ai/) [7]. During my talk, I will provide an overview of the different contributions that are at the base of this digital synthetic chemistry revolution [8].
[1] P. Schwaller et al., ‘Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction’, ACS Cent. Sci., vol. 5, no. 9, pp. 1572–1583, 2019, doi: 10.1021/acscentsci.9b00576.
[2] G. Pesciullesi, P. Schwaller, T. Laino, and J.-L. Reymond, ‘Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates’, Nat. Commun., vol. 11, no. 1, pp. 1–8, 2020.
[3] P. Schwaller et al., ‘Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy’, Chem. Sci., vol. 11, pp. 3316–3325, 2020, doi: 10.1039/C9SC05704H.
[4] A. C. Vaucher, F. Zipoli, J. Geluykens, V. H. Nair, P. Schwaller, and T. Laino, ‘Automated extraction of chemical synthesis actions from experimental procedures’, Nat. Commun., vol. 11, no. 1, p. 3601, Jul. 2020, doi: 10.1038/s41467-020-17266-6.
[5] A. C. Vaucher, P. Schwaller, J. Geluykens, V. H. Nair, A. Iuliano, and T. Laino, ‘Inferring experimental procedures from text-based representations of chemical reactions’, Nat. Commun., vol. 12, no. 1, p. 2573, Dec. 2021, doi: 10.1038/s41467-021-22951-1.
[6] P. Schwaller et al., ‘Mapping the space of chemical reactions using attention-based neural networks’, Nat. Mach. Intell., vol. 3, no. 2, pp. 144–152, Feb. 2021, doi: 10.1038/s42256-020-00284-w.
[7] P. Schwaller, B. Hoover, J.-L. Reymond, H. Strobelt, and T. Laino, ‘Extraction of organic chemistry grammar from unsupervised learning of chemical reactions’, Sci. Adv., vol. 7, no. 15, p. eabe4166, Apr. 2021, doi: 10.1126/sciadv.abe4166.
[8] P. Schwaller et al., ‘Machine intelligence for chemical reaction space’, WIREs Comput. Mol. Sci., Mar. 2022, doi: 10.1002/wcms.1604
Bio: Philippe Schwaller received a bachelor’s and master’s degree in Materials Science and Engineering from EPFL. While working for IBM Research (2017-2021), Philippe completed an MPhil degree in Physics at the University of Cambridge and a PhD in Chemistry and Molecular Sciences with the Reymond group at the University of Bern. In February 2022, Philippe joined EPFL as a tenure-track assistant professor in the Institute of Chemical Sciences and Engineering. He leads the Laboratory of Artificial Chemical Intelligence (LIAC), which works on AI-accelerated discovery and synthesis of molecules. Philippe is also a core PI of the NCCR Catalysis, a Swiss centre for sustainable chemistry research, education, and innovation.
The Center for Intelligent Systems at EPFL (CIS) is a collaboration among IC, ENAC, SB; SV and STI that brings together researchers working on different aspects of Intelligent Systems.
In order to promote exchanges among researchers and encourage the creation of new, collaborative projects, CIS is organizing a "Get to know your neighbors" series. Each seminar will consist of one short overview presentation geared to the general public at EPFL.
The CIS seminar will take place In hybrid mode: Room INF 328 and by Zoom https://epfl.zoom.us/j/63372208916
Please connect to your zoom account using your "@epfl.ch" address, as this live event is only open to the EPFL community
Monday, September 5th, 2022 from 3:15 to 4:15 pm
NB: Video recordings of the seminars will be made available on our website and published on our social media pages
Abstract: In organic chemistry, we are currently witnessing a rise in artificial intelligence (AI) approaches, which show great potential for improving molecular designs, facilitating synthesis, and accelerating the discovery of novel molecules. Based on an analogy between written language and organic chemistry, we built linguistics-inspired Transformer models for chemical reaction prediction [1, 2], synthesis planning [3], and the prediction of experimental actions [4,5]. We extended the models to chemical reaction classification and fingerprints [6]. By finding a mapping from discrete reactions to continuous vectors, we enabled efficient chemical reaction space exploration. Intrigued by the remarkable performance of chemical language models, we discovered that the models can capture how atoms rearrange during a reaction, without supervision or human labelling, leading to the development of the open-source atom-mapping tool RXNMapper (http://rxnmapper.ai/) [7]. During my talk, I will provide an overview of the different contributions that are at the base of this digital synthetic chemistry revolution [8].
[1] P. Schwaller et al., ‘Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction’, ACS Cent. Sci., vol. 5, no. 9, pp. 1572–1583, 2019, doi: 10.1021/acscentsci.9b00576.
[2] G. Pesciullesi, P. Schwaller, T. Laino, and J.-L. Reymond, ‘Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates’, Nat. Commun., vol. 11, no. 1, pp. 1–8, 2020.
[3] P. Schwaller et al., ‘Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy’, Chem. Sci., vol. 11, pp. 3316–3325, 2020, doi: 10.1039/C9SC05704H.
[4] A. C. Vaucher, F. Zipoli, J. Geluykens, V. H. Nair, P. Schwaller, and T. Laino, ‘Automated extraction of chemical synthesis actions from experimental procedures’, Nat. Commun., vol. 11, no. 1, p. 3601, Jul. 2020, doi: 10.1038/s41467-020-17266-6.
[5] A. C. Vaucher, P. Schwaller, J. Geluykens, V. H. Nair, A. Iuliano, and T. Laino, ‘Inferring experimental procedures from text-based representations of chemical reactions’, Nat. Commun., vol. 12, no. 1, p. 2573, Dec. 2021, doi: 10.1038/s41467-021-22951-1.
[6] P. Schwaller et al., ‘Mapping the space of chemical reactions using attention-based neural networks’, Nat. Mach. Intell., vol. 3, no. 2, pp. 144–152, Feb. 2021, doi: 10.1038/s42256-020-00284-w.
[7] P. Schwaller, B. Hoover, J.-L. Reymond, H. Strobelt, and T. Laino, ‘Extraction of organic chemistry grammar from unsupervised learning of chemical reactions’, Sci. Adv., vol. 7, no. 15, p. eabe4166, Apr. 2021, doi: 10.1126/sciadv.abe4166.
[8] P. Schwaller et al., ‘Machine intelligence for chemical reaction space’, WIREs Comput. Mol. Sci., Mar. 2022, doi: 10.1002/wcms.1604
Bio: Philippe Schwaller received a bachelor’s and master’s degree in Materials Science and Engineering from EPFL. While working for IBM Research (2017-2021), Philippe completed an MPhil degree in Physics at the University of Cambridge and a PhD in Chemistry and Molecular Sciences with the Reymond group at the University of Bern. In February 2022, Philippe joined EPFL as a tenure-track assistant professor in the Institute of Chemical Sciences and Engineering. He leads the Laboratory of Artificial Chemical Intelligence (LIAC), which works on AI-accelerated discovery and synthesis of molecules. Philippe is also a core PI of the NCCR Catalysis, a Swiss centre for sustainable chemistry research, education, and innovation.
The Center for Intelligent Systems at EPFL (CIS) is a collaboration among IC, ENAC, SB; SV and STI that brings together researchers working on different aspects of Intelligent Systems.
In order to promote exchanges among researchers and encourage the creation of new, collaborative projects, CIS is organizing a "Get to know your neighbors" series. Each seminar will consist of one short overview presentation geared to the general public at EPFL.
The CIS seminar will take place In hybrid mode: Room INF 328 and by Zoom https://epfl.zoom.us/j/63372208916
Please connect to your zoom account using your "@epfl.ch" address, as this live event is only open to the EPFL community
Monday, September 5th, 2022 from 3:15 to 4:15 pm
NB: Video recordings of the seminars will be made available on our website and published on our social media pages
Practical information
- General public
- Free
Organizer
- CIS