Learning Retrosynthetic Planning through Simulated
Experience
Version 3 2019-06-26, 07:30
Version 2 2019-05-31, 14:04
Version 1 2019-05-31, 13:38
Posted on 2019-06-26 - 07:30
The problem of retrosynthetic
planning can be framed as a one-player
game, in which the chemist (or a computer program) works backward
from a molecular target to simpler starting materials through a series
of choices regarding which reactions to perform. This game is challenging
as the combinatorial space of possible choices is astronomical, and
the value of each choice remains uncertain until the synthesis plan
is completed and its cost evaluated. Here, we address this search
problem using deep reinforcement learning to identify policies that
make (near) optimal reaction choices during each step of retrosynthetic
planning according to a user-defined cost metric. Using a simulated
experience, we train a neural network to estimate the expected synthesis
cost or value of any given molecule based on a representation of its
molecular structure. We show that learned policies based on this value
network can outperform a heuristic approach that favors symmetric
disconnections when synthesizing unfamiliar molecules from available
starting materials using the fewest number of reactions. We discuss
how the learned policies described here can be incorporated into existing
synthesis planning tools and how they can be adapted to changes in
the synthesis cost objective or material availability.
CITE THIS COLLECTION
DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
Schreck, John S.; Coley, Connor W.; J. M. Bishop, Kyle (2019). Learning Retrosynthetic Planning through Simulated
Experience. ACS Publications. Collection. https://doi.org/10.1021/acscentsci.9b00055
or
Select your citation style and then place your mouse over the citation text to select it.
SHARE
Usage metrics
Read the peer-reviewed publication
AUTHORS (3)
JS
John S. Schreck
CC
Connor W. Coley
KJ
Kyle J. M. Bishop