Multi-Objective Coverage via Constraint Active Search

2026-02-17

AI Summary

This work proposes the Multi-Objective Coverage (MOC) problem, which aims to efficiently select a small set of representative samples from the feasible region defined by multiple objectives and safety constraints to accelerate drug discovery and materials design. To address this, the authors introduce the MOC-CAS algorithm, which establishes the first unified framework formally integrating objective-space coverage, constraint handling, and active search. A key innovation is a smooth relaxation strategy for hard feasibility checks. Built upon Gaussian process-based Bayesian optimization, the method employs an upper confidence bound acquisition function to guide the search. Empirical results demonstrate that MOC-CAS significantly outperforms existing baselines across five SMILES-derived objectives on datasets involving SARS-CoV-2 and cancer-related protein targets.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In this paper, we formulate the new multi-objective coverage (MOC) problem where our goal is to identify a small set of representative samples whose predicted outcomes broadly cover the feasible multi-objective space. This problem is of great importance in many critical real-world applications, e.g., drug discovery and materials design, 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.

Problem

• multi-objective coverage
• representative samples
• feasible multi-objective space
• constraint satisfaction
• scientific discovery

Innovation

• multi-objective coverage
• constraint active search
• Gaussian process
• upper confidence bound
• feasibility relaxation