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Computer-aided multi-objective optimization in small molecule discovery
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DOI：10.1016/j.patter.2023.100678.png)
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Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which im-poses assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discov-ery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a rela-tively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportu-nities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi -objec-tive de novo design.
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GENETIC ALGORITHM
DRUG DISCOVERY
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951
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