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ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations | | Ksenia Korovina
; Sailun Xu
; Kirthevasan Kandasamy
; Willie Neiswanger
; Barnabas Poczos
; Jeff Schneider
; Eric P. Xing
; | | Date: |
5 Aug 2019 | | Abstract: | We describe ChemBO, a Bayesian Optimization framework for generating and
optimizing organic molecules for desired molecular properties. This framework
is useful in applications such as drug discovery, where an algorithm recommends
new candidate molecules; these molecules first need to be synthesized and then
tested for drug-like properties. The algorithm uses the results of past tests
to recommend new ones so as to find good molecules efficiently. Most existing
data-driven methods for this problem do not account for sample efficiency
and/or fail to enforce realistic constraints on synthesizability. In this work,
we explore existing kernels for molecules in the literature as well as propose
a novel kernel which views a molecule as a graph. In ChemBO, we implement these
kernels in a Gaussian process model. Then we explore the chemical space by
traversing possible paths of molecular synthesis. Consequently, our approach
provides a proposal synthesis path every time it recommends a new molecule to
test, a crucial advantage when compared to existing methods. In our
experiments, we demonstrate the efficacy of the proposed approach on several
molecular optimization problems. | | Source: | arXiv, 1908.1425 | | Services: | Forum | Review | PDF | Favorites | |
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