Computer-aided multi-objective optimization in small molecule discovery
Name
1-s2.0-S2666389923000016-main.pdf
Description
Published version
Size
2.07 MB
Format
Adobe PDF
Checksum (MD5)
5573df8a317f40e49f7cce6a3009089d
Author(s)
Fromer, Jenna C
Coley, Connor W
Date Issued
February 2023
Journal
Patterns
Publisher
Elsevier BV
Citation
Fromer, Jenna C and Coley, Connor W. 2023. "Computer-aided multi-objective optimization in small molecule discovery." Patterns, 4 (2).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.patter.2023.100678
