# Deep learning-guided evolutionary optimization for protein design

arXiv: https://arxiv.org/abs/2603.02753 · HTML: https://mlsanity.com/paper/2603.02753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [AdaLead: A simple and robust adaptive greedy search algorithm for sequence design](https://mlsanity.com/paper/2010.02141/) (2020)
- [Adaptive machine learning for protein engineering](https://mlsanity.com/paper/2106.05466/) (2021)
- [Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis](https://mlsanity.com/paper/2403.14413/) (2024)
- [Controllable protein design with particle-based Feynman-Kac steering](https://mlsanity.com/paper/2511.09216/) (2025)
- [Protein Sequence Design with Batch Bayesian Optimisation](https://mlsanity.com/paper/2303.10429/) (2023)
- [Evolutionary Algorithms Simulating Molecular Evolution: A New Field Proposal](https://mlsanity.com/paper/2403.08797/) (2024)

## Abstract (arXiv)

Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence-function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data-efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of textitStreptococcus pneumoniae. BoGA accelerates the discovery of high-confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at hrefhttps://github.com/ErikHartman/bopepGitHub.