Computer-Aided Multi-Objective Optimization in Small Molecule Discovery
- URL: http://arxiv.org/abs/2210.07209v1
- Date: Thu, 13 Oct 2022 17:33:07 GMT
- Title: Computer-Aided Multi-Objective Optimization in Small Molecule Discovery
- Authors: Jenna C. Fromer and Connor W. Coley
- Abstract summary: We describe pool-based and de novo generative approaches to multi-objective molecular discovery.
We show how pool-based molecular discovery is a relatively direct extension of multi-objective Bayesian optimization.
We discuss some remaining challenges and opportunities in the field.
- Score: 3.032184156362992
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 imposes 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 discovery with a
focus on Pareto optimization algorithms. We show how pool-based molecular
discovery is a relatively 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 opportunities in
the field, emphasizing the opportunity to adopt Bayesian optimization
techniques into multi-objective de novo design.
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