# ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods

arXiv: https://arxiv.org/abs/2505.22494 · HTML: https://mlsanity.com/paper/2505.22494/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 80 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 30 of 30)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [A Continuous Time Framework for Discrete Denoising Models](https://mlsanity.com/paper/2205.14987/) (2022)
- [Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem](https://mlsanity.com/paper/2206.04119/) (2022)
- [Practical and Asymptotically Exact Conditional Sampling in Diffusion Models](https://mlsanity.com/paper/2306.17775/) (2023)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [Autoregressive Diffusion Models](https://mlsanity.com/paper/2110.02037/) (2021)
- [Biological Sequence Design with GFlowNets](https://mlsanity.com/paper/2203.04115/) (2022)
- [Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding](https://mlsanity.com/paper/2408.08252/) (2024)
- [Unlocking Guidance for Discrete State-Space Diffusion and Flow Models](https://mlsanity.com/paper/2406.01572/) (2024)
- [Conditioning by adaptive sampling for robust design](https://mlsanity.com/paper/1901.10060/) (2019)
- [Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders](https://mlsanity.com/paper/2203.12742/) (2022)
- [Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models](https://mlsanity.com/paper/2211.17091/) (2022)
- [Neural Machine Translation in Linear Time](https://mlsanity.com/paper/1610.10099/) (2016)
- [AdaLead: A simple and robust adaptive greedy search algorithm for sequence design](https://mlsanity.com/paper/2010.02141/) (2020)
- [Elements of Sequential Monte Carlo](https://mlsanity.com/paper/1903.04797/) (2019)
- [Protein Discovery with Discrete Walk-Jump Sampling](https://mlsanity.com/paper/2306.12360/) (2023)
- [Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo](https://mlsanity.com/paper/2502.06079/) (2025)
- [Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design](https://mlsanity.com/paper/2502.14944/) (2025)
- [Improving Protein Optimization with Smoothed Fitness Landscapes](https://mlsanity.com/paper/2307.00494/) (2023)
- [Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space](https://mlsanity.com/paper/2405.18986/) (2024)
- [Improved Off-policy Reinforcement Learning in Biological Sequence Design](https://mlsanity.com/paper/2410.04461/) (2024)
- [Importance Weighted Expectation-Maximization for Protein Sequence Design](https://mlsanity.com/paper/2305.00386/) (2023)
- [Unifying Likelihood-free Inference with Black-box Optimization and Beyond](https://mlsanity.com/paper/2110.03372/) (2021)
- [Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences](https://mlsanity.com/paper/2412.07763/) (2024)
- [Discriminator Guidance for Autoregressive Diffusion Models](https://mlsanity.com/paper/2310.15817/) (2023)
- [Beyond the training set: an intuitive method for detecting distribution shift in model-based optimization](https://mlsanity.com/paper/2311.05363/) (2023)
- [Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking](https://mlsanity.com/paper/2502.17246/) (2025)
- [Robust Model-Based Optimization for Challenging Fitness Landscapes](https://mlsanity.com/paper/2305.13650/) (2023)
- [Active Learning Enables Generation of Molecules that Advance the Known Pareto Front](https://mlsanity.com/paper/2501.02059/) (2025)

## Built on by (most cited first, 3 of 3)

- [seqme: a Python library for evaluating biological sequence design](https://mlsanity.com/paper/2511.04239/) (2025)
- [Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets](https://mlsanity.com/paper/2605.26690/) (2026)
- [Feedback-Calibrated Protein Optimization with Batch-Aligned Tail Arbitration](https://mlsanity.com/paper/2609.37808/) (2026)

## Abstract (arXiv)

Designing protein sequences of both high fitness and novelty is a challenging task in data-efficient protein engineering. Exploration beyond wild-type neighborhoods often leads to biologically implausible sequences or relies on surrogate models that lose fidelity in novel regions. Here, we propose ProSpero, an active learning framework in which a frozen pre-trained generative model is guided by a surrogate updated from oracle feedback. By integrating fitness-relevant residue selection with biologically-constrained Sequential Monte Carlo sampling, our approach enables exploration beyond wild-type neighborhoods while preserving biological plausibility. We show that our framework remains effective even when the surrogate is misspecified. ProSpero consistently outperforms or matches existing methods across diverse protein engineering tasks, retrieving sequences of both high fitness and novelty.