Multi-Objective Coverage via Constraint Active Search

Inthispaper, weformulatethenewmulti-objectivecoverage(MOC) problem where our goal is to identify a small set of representative samples whose predicted outcomes broadly cover the feasible multiobjectivespace. Thisproblemisofgreatimportanceinmanycritical real-worldapplications, e. g. ,drugdiscoveryandmaterialsdesign, as this representative set can be evaluated much faster than the whole feasible set, thus significantly accelerating the scientific discovery process. Existing works cannot be directly applied as they either focus on sample space coverage or multi-objective optimization that targets the Pareto front. However, chemically diverse samples often yield identical objective profiles, and safety constraints are usually defined on the objectives. To solve this MOC problem, we propose a novel search algorithm, MOC-CAS, which employs an upper confidence bound-based acquisition function to select optimistic samples guided by Gaussian process posterior predictions. For enabling efficient optimization, we develop a smoothed relaxation of the hard feasibility test and derive an approximate optimizer. Compared to the competitive baselines, we show that our MOC-CAS empirically achieves superior performances across large-scale protein-target datasets for SARS-CoV-2 and cancer, each assessed on five objectives derived from SMILES-based features.

Authors

• Zakaria Shams Siam University at Albany, State University of New York
• Xuefeng Liu University of Chicago
• Chong Liu University at Albany, State University of New York

Keywords

• Multi-objective coverage
• Constraint active search
• Upper confidence bound
• Gaussian processes
• Drug discovery