# A primer on model-guided exploration of fitness landscapes for biological sequence design

arXiv: https://arxiv.org/abs/2010.10614 · HTML: https://mlsanity.com/paper/2010.10614/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design](https://mlsanity.com/paper/2202.03613/) (2022)
- [PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design](https://mlsanity.com/paper/2210.04096/) (2022)
- [Parallelizing Contextual Bandits](https://mlsanity.com/paper/2105.10590/) (2021)
- [Beyond the training set: an intuitive method for detecting distribution shift in model-based optimization](https://mlsanity.com/paper/2311.05363/) (2023)
- [A Variational Perspective on Generative Protein Fitness Optimization](https://mlsanity.com/paper/2501.19200/) (2025)
- [Improving few-shot learning-based protein engineering with evolutionary sampling](https://mlsanity.com/paper/2305.15441/) (2023)
- [Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization](https://mlsanity.com/paper/2602.02425/) (2026)

## Abstract (arXiv)

Machine learning methods are increasingly employed to address challenges faced by biologists. One area that will greatly benefit from this cross-pollination is the problem of biological sequence design, which has massive potential for therapeutic applications. However, significant inefficiencies remain in communication between these fields which result in biologists finding the progress in machine learning inaccessible, and hinder machine learning scientists from contributing to impactful problems in bioengineering. Sequence design can be seen as a search process on a discrete, high-dimensional space, where each sequence is associated with a function. This sequence-to-function map is known as a "Fitness Landscape". Designing a sequence with a particular function is hence a matter of "discovering" such a (often rare) sequence within this space. Today we can build predictive models with good interpolation ability due to impressive progress in the synthesis and testing of biological sequences in large numbers, which enables model training and validation. However, it often remains a challenge to find useful sequences with the properties that we like using these models. In particular, in this primer we highlight that algorithms for experimental design, what we call "exploration strategies", are a related, yet distinct problem from building good models of sequence-to-function maps. We review advances and insights from current literature -- by no means a complete treatment -- while highlighting desirable features of optimal model-guided exploration, and cover potential pitfalls drawn from our own experience. This primer can serve as a starting point for researchers from different domains that are interested in the problem of searching a sequence space with a model, but are perhaps unaware of approaches that originate outside their field.