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SUMMARY:CIS - "Get to know your neighbors" Seminar series - Prof. Philippe
  Schwaller
DTSTART:20220905T151500
DTEND:20220905T161500
DTSTAMP:20260916T065538Z
UID:deab3b435fa34febe4d8f90a422400d39ced2b9e86dacf716171b18e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Philippe Schwaller\nTitle: Accelerating Chemical Synthes
 is with Transformers\n\nAbstract: 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 b
 etween written language and organic chemistry\, we built linguistics-inspi
 red Transformer models for chemical reaction prediction [1\, 2]\, synthesi
 s planning [3]\, and the prediction of experimental actions [4\,5]. We ex
 tended 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 r
 emarkable performance of chemical language models\, we discovered that the
  models can capture how atoms rearrange during a reaction\, without super
 vision 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].\n\n[1]       P
 . Schwaller et al.\, ‘Molecular Transformer: A Model for Uncertainty-Cal
 ibrated Chemical Reaction Prediction’\, ACS Cent. Sci.\, vol. 5\, no. 9\
 , pp. 1572–1583\, 2019\, doi: 10.1021/acscentsci.9b00576.\n[2]      
  G. Pesciullesi\, P. Schwaller\, T. Laino\, and J.-L. Reymond\, ‘Transfe
 r learning enables the molecular transformer to predict regio-and stereose
 lective reactions on carbohydrates’\, Nat. Commun.\, vol. 11\, no. 1\, 
 pp. 1–8\, 2020.\n[3]       P. Schwaller et al.\, ‘Predicting retros
 ynthetic pathways using transformer-based models and a hyper-graph explora
 tion strategy’\, Chem. Sci.\, vol. 11\, pp. 3316–3325\, 2020\, doi: 1
 0.1039/C9SC05704H.\n[4]       A. C. Vaucher\, F. Zipoli\, J. Geluykens\
 , V. H. Nair\, P. Schwaller\, and T. Laino\, ‘Automated extraction of ch
 emical synthesis actions from experimental procedures’\, Nat. Commun.\,
  vol. 11\, no. 1\, p. 3601\, Jul. 2020\, doi: 10.1038/s41467-020-17266-6.\
 n[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.\n[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.\n[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.\n[8]       P. Schwaller et al.\, 
 ‘Machine intelligence for chemical reaction space’\, WIREs Comput. Mol
 . Sci.\, Mar. 2022\, doi: 10.1002/wcms.1604\n\nBio: Philippe Schwaller rec
 eived a bachelor’s and master’s degree in Materials Science and Engine
 ering from EPFL. While working for IBM Research (2017-2021)\, Philippe co
 mpleted an MPhil degree in Physics at the University of Cambridge and a Ph
 D in Chemistry and Molecular Sciences with the Reymond group at the Unive
 rsity of Bern. In February 2022\, Philippe joined EPFL as a tenure-track a
 ssistant professor in the Institute of Chemical Sciences and Engineering. 
 He leads the Laboratory of Artificial Chemical Intelligence (LIAC)\, whic
 h works on AI-accelerated discovery and synthesis of molecules. Philippe i
 s also a core PI of the NCCR Catalysis\, a Swiss centre for sustainable c
 hemistry research\, education\, and innovation. \n\n\n\nThe Center for In
 telligent Systems at EPFL (CIS) is a collaboration among IC\, ENAC\, SB\; 
 SV and STI that brings together researchers working on different aspects o
 f Intelligent Systems.\nIn order to promote exchanges among researchers an
 d encourage the creation of new\, collaborative projects\, CIS is organizi
 ng a "Get to know your neighbors" series. Each seminar will consist of one
  short overview presentation geared to the general public at EPFL.   \n
  \nThe CIS seminar will take place In hybrid mode: Room INF 328 and by Zo
 om https://epfl.zoom.us/j/63372208916\n\nPlease connect to your zoom accou
 nt using your "@epfl.ch" address\, as this live event is only open to the 
 EPFL community\nMonday\, September 5th\, 2022 from 3:15 to 4:15 pm\nNB: Vi
 deo recordings of the seminars will be made available on our website and p
 ublished on our social media pages
LOCATION:INF 328 https://plan.epfl.ch/?room==INF%20328 https://epfl.zoom.u
 s/j/63372208916
STATUS:CONFIRMED
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