Leveraging Discrete Function Decomposability for Scientific Design

2025-11-04

AI Summary

Discrete-space optimization in AI-driven scientific design—e.g., protein sequence and circuit layout design—suffers from low efficiency, as existing distribution optimization methods fail to exploit the inherent decomposability of property prediction models. Method: We propose Decomposition-Aware Distribution Optimization (DADO), the first method to integrate junction-tree-guided soft factorization of the search distribution with graph neural message passing, explicitly modeling local dependencies among design variables. DADO unifies estimation-of-distribution algorithms with a reinforcement learning framework, leveraging a learnable graph neural network generative model for efficient navigation in discrete design spaces. Contribution/Results: Across multiple scientific design benchmarks, DADO achieves significantly faster convergence and higher-quality optima compared to state-of-the-art baselines. Empirical results demonstrate that explicitly incorporating functional decomposability into distribution optimization is both effective and broadly applicable for discrete scientific design tasks.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For example, we may wish to design a protein to bind its target, arrange components within a circuit to minimize latency, or find materials with certain properties. Given a property predictive model, in silico design typically involves training a generative model over the design space (e.g., protein sequence space) to concentrate on designs with the desired properties. Distributional optimization -- which can be formalized as an estimation of distribution algorithm or as reinforcement learning policy optimization -- finds the generative model that maximizes an objective function in expectation. Optimizing a distribution over discrete-valued designs is in general challenging because of the combinatorial nature of the design space. However, many property predictors in scientific applications are decomposable in the sense that they can be factorized over design variables in a way that could in principle enable more effective optimization. For example, amino acids at a catalytic site of a protein may only loosely interact with amino acids of the rest of the protein to achieve maximal catalytic activity. Current distributional optimization algorithms are unable to make use of such decomposability structure. Herein, we propose and demonstrate use of a new distributional optimization algorithm, Decomposition-Aware Distributional Optimization (DADO), that can leverage any decomposability defined by a junction tree on the design variables, to make optimization more efficient. At its core, DADO employs a soft-factorized"search distribution"-- a learned generative model -- for efficient navigation of the search space, invoking graph message-passing to coordinate optimization across linked factors.

Problem

• Optimizing discrete object design via distributional algorithms
• Leveraging decomposable property predictors for efficient optimization
• Addressing combinatorial challenges in scientific generative modeling

Innovation

• Uses decomposability via junction tree structure
• Employs soft-factorized search distribution model
• Leverages graph message-passing for coordinated optimization