mlsanityData to 7 Oct 2026

Adaptive machine learning for protein engineering

arXiv 2106.05466 ↗First posted 10 Jun 2021q-bio.QM cs.LG q-bio.BMcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 20 of its 79 references that are in the map. First posted on arXiv 10 Jun 2021.

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20 of 79 references.

References · 20

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
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  12. 2019Combinatorial Bayesian Optimization using the Graph Cartesian Productcited by 78
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  1. 2020AdaLead: A simple and robust adaptive greedy search algorithm for sequence designcited by 63
  2. 2020BOSS: Bayesian Optimization over String Spacescited by 54
  3. 2019Learning protein sequence embeddings using information from structurecited by 50
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  8. 2019Batched Stochastic Bayesian Optimization via Combinatorial Constraints Designcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 7; 2024: 1; 2025: 2; 2026: 1.

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  1. 2024Empowering Biomedical Discovery with AI Agentscited by 117
  2. 2021Deep Extrapolation for Attribute-Enhanced Generationcited by 18
  3. 2023Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusioncited by 9
  4. 2025Steering Generative Models with Experimental Data for Protein Fitness Optimizationcited by 7
  5. 2023Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Explorationcited by 5
  6. 2023Generative artificial intelligence for de novo protein designcited by 5
  7. 2023Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretabilitycited by 2
  8. 2023Robust Model-Based Optimization for Challenging Fitness Landscapescited by 2
  9. 2023Insights Into the Inner Workings of Transformer Models for Protein Function Predictioncited by 2
  10. 2023Protein Sequence Design with Batch Bayesian Optimisationcited by 1
  11. 2022A generative recommender system with GMM prior for cancer drug generation and sensitivity predictioncited by 0
  12. 2025Low-N Protein Activity Optimization with FolDEcited by 0
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  1. 2026Deep learning-guided evolutionary optimization for protein designcited by 0