Related papers: Barking up the right tree: an approach to search over molecule synthesis DAGs

Barking up the right tree: an approach to search over molecule synthesis DAGs

URL: http://arxiv.org/abs/2012.11522v1
Date: Mon, 21 Dec 2020 17:35:06 GMT
Title: Barking up the right tree: an approach to search over molecule synthesis DAGs
Authors: John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, Jos\'e Miguel Hern\'andez-Lobato
Abstract summary: Current deep generative models for molecules ignore synthesizability. We propose a deep generative model that better represents the real world process. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules.
Score: 28.13323960125482
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple building blocks can be recursively combined through chemical reactions to create more complicated molecules of interest. In contrast, many current deep generative models for molecules ignore synthesizability. We therefore propose a deep generative model that better represents the real world process, by directly outputting molecule synthesis DAGs. We argue that this provides sensible inductive biases, ensuring that our model searches over the same chemical space that chemists would also have access to, as well as interpretability. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules, and allows for unconstrained optimization of an inherently constrained problem: maximize certain chemical properties such that discovered molecules are synthesizable.

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