(1/5) The latest from our group: Jenna Fromer's overview of computer-aided multi-objective optimization in small molecule discovery is now online & open access @Patterns_CP | doi.org/10.1016/j.patt… #compchem 
(2/5) Our focus here is on Pareto optimization. Pareto optimization introduces additional algorithmic complexities, but reveals more information about the trade-offs between objectives and is more robust than scalarization approaches
(3/5) We highlight the extensions from single-objective Bayesian optimization to multi-objective Bayesian optimization when choosing molecules from a discrete library. The primary difference is the definition of the acquisition function, with a few options listed in the fig above
(4/5) We also describe the main categories of approaches in multi-objective generative design, _most of which_ follow the paradigm of "iterative distribution learning", & illustrate them through a few case studies
(5/5) Molecular design is fundamentally a multi-objective problem. We hope this will be a useful reference for folks looking to get into computer-aided molecular design and/or move away from scalarization
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