ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
This work addresses the challenge of sample-efficient and synthesizable molecule design for applications like drug discovery, representing an incremental improvement over existing methods.
The authors tackled the problem of designing organic molecules with desired properties by introducing ChemBO, a Bayesian optimization framework that efficiently explores the synthesis graph to produce synthesizable candidates, achieving improved sample efficiency and performance on molecular optimization tasks.
In applications such as molecule design or drug discovery, it is desirable to have an algorithm which recommends new candidate molecules based on the results of past tests. These molecules first need to be synthesized and then tested for objective properties. We describe ChemBO, a Bayesian optimization framework for generating and optimizing organic molecules for desired molecular properties. While most existing data-driven methods for this problem do not account for sample efficiency or fail to enforce realistic constraints on synthesizability, our approach explores the synthesis graph in a sample-efficient way and produces synthesizable candidates. We implement ChemBO as a Gaussian process model and explore existing molecular kernels for it. Moreover, we propose a novel optimal-transport based distance and kernel that accounts for graphical information explicitly. In our experiments, we demonstrate the efficacy of the proposed approach on several molecular optimization problems.