Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design (2505.07086v2)

Abstract: Designing biological sequences that satisfy multiple, often conflicting, functional and biophysical criteria remains a central challenge in biomolecule engineering. While discrete flow matching models have recently shown promise for efficient sampling in high-dimensional sequence spaces, existing approaches address only single objectives or require continuous embeddings that can distort discrete distributions. We present Multi-Objective-Guided Discrete Flow Matching (MOG-DFM), a general framework to steer any pretrained discrete flow matching generator toward Pareto-efficient trade-offs across multiple scalar objectives. At each sampling step, MOG-DFM computes a hybrid rank-directional score for candidate transitions and applies an adaptive hypercone filter to enforce consistent multi-objective progression. We also trained two unconditional discrete flow matching models, PepDFM for diverse peptide generation and EnhancerDFM for functional enhancer DNA generation, as base generation models for MOG-DFM. We demonstrate MOG-DFM's effectiveness in generating peptide binders optimized across five properties (hemolysis, non-fouling, solubility, half-life, and binding affinity), and in designing DNA sequences with specific enhancer classes and DNA shapes. In total, MOG-DFM proves to be a powerful tool for multi-property-guided biomolecule sequence design.

Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design

The research paper explores the intricate challenge of designing biological sequences that meet multiple functional and biophysical criteria through the introduction of Multi-Objective-Guided Discrete Flow Matching (MOG-DFM). This paper ventures into the domain of biomolecule engineering, striving to optimize various conflicting properties simultaneously — a task essential for developing effective biomolecules such as therapeutic peptides or functional DNA enhancers.

Methodological Framework

The method utilized in this research hinges on discrete flow matching, a recent innovation in sampling from high-dimensional discrete spaces efficiently. Traditional approaches to sequence design typically concentrate on optimizing a single objective, often leading to sequences plagued by trade-offs. MOG-DFM, however, propounds a solution that incorporates a Pareto-efficient framework capable of handling multiple scalar objectives by steering a pre-trained discrete-time flow matching generator.

The central tenets of the MOG-DFM approach include:

1. Rank-Directional Scoring: This combines rank normalized local improvement with directional alignment towards a predefined trade-off vector, allowing the balancing of multiple targets.
2. Adaptive Hypercone Filtering: Ensures consistent multi-objective progression by applying a hypercone filter that influences transition steps based on their alignment with desired trade-offs.
3. Unconditional Base Models: The paper additionally trains two base models — PepDFM for diversified peptide generation and EnhancerDFM for functional DNA sequence design — demonstrating all models maintain biological plausibility and low prediction error rates.

Experimental Evaluation and Results

The research conducts extensive experiments to validate the effectiveness of MOG-DFM across specific generation tasks: peptide binders and enhancer DNA sequences.

1. Peptide Binder Generation: The framework optimizes five properties crucial for therapeutic applications: hemolysis, solubility, binding affinity, half-life, and non-fouling capacity. MOG-DFM effectively generated peptides with improved properties by strategically navigating the solution space toward a balanced Pareto frontier.
2. Enhancer DNA Sequence Generation: This task aimed to direct enhancer DNA sequences towards specific biological functions and shapes. MOG-DFM successfully achieved targeted enhancer class probabilities and DNA shapes, further substantiating its versatile applicability.

The paper claims that MOG-DFM generally outperforms classic evolutionary algorithms and recent flow-based approaches designed for similar tasks, achieving superior empirical results on multiple fronts without compromising the stability or robustness of generation.

Implications and Future Directions

The implications of this research are broad, impacting both theoretical aspects of multi-objective optimization and practical applications in bioengineering. By effectively managing to align diverse biological targets with computational efficient generative models, MOG-DFM sets a precedent for future explorations in sequence design, especially in areas demanding comprehensive property optimization.

Moving forward, researchers could expand MOG-DFM's applicability to more complex, higher-dimensional biological sequences, leveraging its potential in diverse fields such as synthetic biology, genetic engineering, and pharmaceutical development. Future work may focus on incorporating these findings to improve reliability in uncertain environments or feedback-driven model adjustments, further integrating such technologies into practical applications requiring robust and flexible multi-objective optimization frameworks.

This paper provides a compelling view of how discrete flow matching and multi-objective optimization can cohesively transform biomolecular engineering tasks, enhancing the capability to design sequences that are not only functionally optimal but also aligned with several critical biological properties, paving the way for novel therapeutic discoveries.