Amortized Tree Generation for Bottom-up Synthesis Planning and
Synthesizable Molecular Design
- URL: http://arxiv.org/abs/2110.06389v1
- Date: Tue, 12 Oct 2021 22:43:25 GMT
- Title: Amortized Tree Generation for Bottom-up Synthesis Planning and
Synthesizable Molecular Design
- Authors: Wenhao Gao, Roc\'io Mercado and Connor W. Coley
- Abstract summary: We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding.
This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes.
- Score: 2.17167311150369
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Molecular design and synthesis planning are two critical steps in the process
of molecular discovery that we propose to formulate as a single shared task of
conditional synthetic pathway generation. We report an amortized approach to
generate synthetic pathways as a Markov decision process conditioned on a
target molecular embedding. This approach allows us to conduct synthesis
planning in a bottom-up manner and design synthesizable molecules by decoding
from optimized conditional codes, demonstrating the potential to solve both
problems of design and synthesis simultaneously. The approach leverages neural
networks to probabilistically model the synthetic trees, one reaction step at a
time, according to reactivity rules encoded in a discrete action space of
reaction templates. We train these networks on hundreds of thousands of
artificial pathways generated from a pool of purchasable compounds and a list
of expert-curated templates. We validate our method with (a) the recovery of
molecules using conditional generation, (b) the identification of synthesizable
structural analogs, and (c) the optimization of molecular structures given
oracle functions relevant to drug discovery.
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