RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software-Reference-Cited by-同舟云学术

RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software

Author:

Liu Cheng-Hao¹²^ORCID,Korablyov Maksym¹,Jastrzębski Stanisław³⁴,Włodarczyk-Pruszyński Paweł³,Bengio Yoshua¹,Segler Marwin⁵⁶^ORCID

Affiliation:

1. Mila and Université de Montréal, 6666 St-Urbain Street, Montreal, Canada H2S 3H1

2. Department of Chemistry, McGill University, 801 Sherbooke Street W, Montreal, Canada H3A 0B8

3. Molecule.one, Warsaw 00-815, Poland

4. Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland

5. Institute of Organic Chemistry and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, 48149 Münster, Germany

6. Microsoft Research, 21 Station Road, Cambridge, U.K. CB1 2FB

Funder

Government of Canada

Institut de Valorisation des Donn?es

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

Reference46 articles.

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