# Adaptive machine learning for protein engineering

arXiv: https://arxiv.org/abs/2106.05466 · HTML: https://mlsanity.com/paper/2106.05466/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 79 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 20 of 20)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [ProGen: Language Modeling for Protein Generation](https://mlsanity.com/paper/2004.03497/) (2020)
- [Maximizing acquisition functions for Bayesian optimization](https://mlsanity.com/paper/1805.10196/) (2018)
- [Conditioning by adaptive sampling for robust design](https://mlsanity.com/paper/1901.10060/) (2019)
- [Batch Bayesian Optimization via Local Penalization](https://mlsanity.com/paper/1505.08052/) (2015)
- [Model Inversion Networks for Model-Based Optimization](https://mlsanity.com/paper/1912.13464/) (2019)
- [Combinatorial Bayesian Optimization using the Graph Cartesian Product](https://mlsanity.com/paper/1902.00448/) (2019)
- [AdaLead: A simple and robust adaptive greedy search algorithm for sequence design](https://mlsanity.com/paper/2010.02141/) (2020)
- [BOSS: Bayesian Optimization over String Spaces](https://mlsanity.com/paper/2010.00979/) (2020)
- [Learning protein sequence embeddings using information from structure](https://mlsanity.com/paper/1902.08661/) (2019)
- [Autofocused oracles for model-based design](https://mlsanity.com/paper/2006.08052/) (2020)
- [Protein sequence design with deep generative models](https://mlsanity.com/paper/2104.04457/) (2021)
- [Design by adaptive sampling](https://mlsanity.com/paper/1810.03714/) (2018)
- [Population-Based Black-Box Optimization for Biological Sequence Design](https://mlsanity.com/paper/2006.03227/) (2020)
- [Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design](https://mlsanity.com/paper/1904.08102/) (2019)

## Built on by (most cited first, 13 of 13)

- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [Deep Extrapolation for Attribute-Enhanced Generation](https://mlsanity.com/paper/2107.02968/) (2021)
- [Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion](https://mlsanity.com/paper/2401.06151/) (2023)
- [Steering Generative Models with Experimental Data for Protein Fitness Optimization](https://mlsanity.com/paper/2505.15093/) (2025)
- [Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration](https://mlsanity.com/paper/2302.02399/) (2023)
- [Generative artificial intelligence for de novo protein design](https://mlsanity.com/paper/2310.09685/) (2023)
- [Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability](https://mlsanity.com/paper/2302.03294/) (2023)
- [Robust Model-Based Optimization for Challenging Fitness Landscapes](https://mlsanity.com/paper/2305.13650/) (2023)
- [Insights Into the Inner Workings of Transformer Models for Protein Function Prediction](https://mlsanity.com/paper/2309.03631/) (2023)
- [Protein Sequence Design with Batch Bayesian Optimisation](https://mlsanity.com/paper/2303.10429/) (2023)
- [A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction](https://mlsanity.com/paper/2206.03555/) (2022)
- [Low-N Protein Activity Optimization with FolDE](https://mlsanity.com/paper/2510.24053/) (2025)
- [Deep learning-guided evolutionary optimization for protein design](https://mlsanity.com/paper/2603.02753/) (2026)

## Abstract (arXiv)

Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatorial complexity of protein sequences. Here, we review how to use a sequence-to-function machine-learning surrogate model to select sequences for experimental measurement. First, we discuss how to select sequences through a single round of machine-learning optimization. Then, we discuss sequential optimization, where the goal is to discover optimized sequences and improve the model across multiple rounds of training, optimization, and experimental measurement.