Multi-Objective Coverage via Constraint Active Search
arXiv:2602.15595v1 Announce Type: new 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 g
arXiv:2602.15595v1 Announce Type: new 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.
Executive Summary
This paper proposes a novel search algorithm, MOC-CAS, to address the multi-objective coverage (MOC) problem, which involves identifying a small set of representative samples to cover the feasible multi-objective space. The algorithm employs an upper confidence bound-based acquisition function and a smoothed relaxation of the hard feasibility test, achieving superior performance in large-scale protein-target datasets for SARS-CoV-2 and cancer. The MOC-CAS algorithm has the potential to significantly accelerate scientific discovery processes in critical real-world applications such as drug discovery and materials design. However, its scalability and applicability to more complex systems remain to be explored.
Key Points
- ▸ The MOC-CAS algorithm addresses the multi-objective coverage problem in real-world applications.
- ▸ The algorithm employs an upper confidence bound-based acquisition function and a smoothed relaxation of the hard feasibility test.
- ▸ MOC-CAS achieves superior performance in large-scale protein-target datasets for SARS-CoV-2 and cancer.
Merits
Strength in Addressing a Critical Problem
The MOC-CAS algorithm is designed to tackle the multi-objective coverage problem, which is a significant challenge in real-world applications such as drug discovery and materials design. The algorithm's ability to address this problem effectively is a major strength.
Innovative Acquisition Function
The upper confidence bound-based acquisition function employed by MOC-CAS is an innovative approach to sample selection, which enables the algorithm to efficiently explore the multi-objective space.
Demerits
Limited Scalability
While MOC-CAS achieves superior performance in large-scale protein-target datasets, its scalability and applicability to more complex systems remain to be explored. Further research is needed to assess the algorithm's performance in more challenging scenarios.
Assumptions on Gaussian Process Posterior Predictions
The algorithm relies on Gaussian process posterior predictions, which may not always be accurate or reliable. Further research is needed to investigate the robustness of MOC-CAS under different data distributions.
Expert Commentary
The MOC-CAS algorithm is a significant contribution to the field of multi-objective optimization, as it addresses a critical problem in real-world applications. While the algorithm's performance is promising, further research is needed to assess its scalability and robustness under different data distributions. Additionally, the implications of MOC-CAS for scientific discovery and policy-making are significant, highlighting the need for more effective multi-objective optimization algorithms.
Recommendations
- ✓ Further research is needed to investigate the scalability and robustness of MOC-CAS in more complex systems.
- ✓ The algorithm should be applied to a wider range of real-world applications to assess its effectiveness and identify potential limitations.
