BioBO: Biology-informed Bayesian Optimization for Perturbation Design
This work addresses the challenge of accelerating drug discovery and therapeutic target identification for researchers, though it is incremental as it builds on existing Bayesian optimization methods by incorporating domain-specific knowledge.
The paper tackles the problem of efficiently designing genomic perturbation experiments for drug discovery by proposing BioBO, a method that integrates Bayesian optimization with biological prior knowledge, resulting in a 25-40% improvement in labeling efficiency and better identification of top-performing perturbations compared to conventional BO.
Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human genome remains infeasible due to the vast search space of potential genetic interactions and experimental constraints. Bayesian optimization (BO) has emerged as a powerful framework for selecting informative interventions, but existing approaches often fail to exploit domain-specific biological prior knowledge. We propose Biology-Informed Bayesian Optimization (BioBO), a method that integrates Bayesian optimization with multimodal gene embeddings and enrichment analysis, a widely used tool for gene prioritization in biology, to enhance surrogate modeling and acquisition strategies. BioBO combines biologically grounded priors with acquisition functions in a principled framework, which biases the search toward promising genes while maintaining the ability to explore uncertain regions. Through experiments on established public benchmarks and datasets, we demonstrate that BioBO improves labeling efficiency by 25-40%, and consistently outperforms conventional BO by identifying top-performing perturbations more effectively. Moreover, by incorporating enrichment analysis, BioBO yields pathway-level explanations for selected perturbations, offering mechanistic interpretability that links designs to biologically coherent regulatory circuits.