US 12,217,834 B2
Molecular graph generation from structural features using an artificial neural network
Paul Maragakis, New York, NY (US); Hunter Nisonoff, New York, NY (US); Peter Skopp, New York, NY (US); and John Salmon, New York, NY (US)
Assigned to D. E. Shaw Research, LLC, New York, NY (US)
Appl. No. 17/614,856
Filed by D. E. Shaw Research, LLC, New York, NY (US)
PCT Filed May 29, 2020, PCT No. PCT/US2020/035137
§ 371(c)(1), (2) Date Nov. 29, 2021,
PCT Pub. No. WO2020/243440, PCT Pub. Date Dec. 3, 2020.
Claims priority of provisional application 62/855,355, filed on May 31, 2019.
Claims priority of provisional application 62/855,388, filed on May 31, 2019.
Prior Publication US 2022/0230713 A1, Jul. 21, 2022
Int. Cl. G16C 20/30 (2019.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G16C 20/50 (2019.01); G16C 20/70 (2019.01); G16C 20/80 (2019.01)
CPC G16C 20/30 (2019.02) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G16C 20/50 (2019.02); G16C 20/70 (2019.02); G16C 20/80 (2019.02)] 31 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating a molecular graph comprising:
obtaining a data representation of first structural features of a first molecule;
processing, using a computer, the data representation of the first structural features of the first molecule to yield a first latent representation of the first molecule; and
processing, using the computer, the first latent representation to yield a data representation of a molecular graph of at least one molecule matching the first structural features;
wherein the first structural features comprises conformational properties of a conformation of a molecule.