mlsanityData to 14 Oct 2026

Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space

arXiv 2405.18986 ↗First posted 29 May 2024cs.LG q-bio.BM q-bio.QMcited by 13

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using computational methods remains challenging, especially when starting from low-fitness sequences. We propose LatProtRL, an optimization method to efficiently traverse a latent space learned by an encoder-decoder leveraging a large protein language model. To escape local optima, our optimization is modeled as a Markov decision process using reinforcement learning acting directly in latent space. We evaluate our approach on two important fitness optimization tasks, demonstrating its ability to achieve comparable or superior fitness over baseline methods. Our findings and in vitro evaluation show that the generated sequences can reach high-fitness regions, suggesting a substantial potential of LatProtRL in lab-in-the-loop scenarios.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 38 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

16 of 38 references.

References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  3. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  4. 2020First return, then explorecited by 266
  5. 2019Evaluating Protein Transfer Learning with TAPEcited by 231
  6. 2022Biological Sequence Design with GFlowNetscited by 174
  7. 2019Conditioning by adaptive sampling for robust designcited by 111
  8. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 102
  9. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Modelscited by 98
  10. 2022Tranception: protein fitness prediction with autoregressive transformers and inference-time retrievalcited by 79
  11. 2020AdaLead: A simple and robust adaptive greedy search algorithm for sequence designcited by 65
  12. 2023Protein Discovery with Discrete Walk-Jump Samplingcited by 38
4 more
  1. 2023Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequencescited by 23
  2. 2023Improving Protein Optimization with Smoothed Fitness Landscapescited by 21
  3. 2023[CLS] Token is All You Need for Zero-Shot Semantic Segmentationcited by 5
  4. 2022ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteinscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 5.

13 papers.

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  1. 2025Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modelingcited by 27
  2. 2024Improved Off-policy Reinforcement Learning in Biological Sequence Designcited by 13
  3. 2025Steering Protein Language Modelscited by 7
  4. 2025Regulatory DNA sequence Design with Reinforcement Learningcited by 6
  5. 2025PDFBench: A Benchmark for De novo Protein Design from Functioncited by 5
  6. 2025A Variational Perspective on Generative Protein Fitness Optimizationcited by 4
  7. 2025ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoodscited by 3
  8. 2024GROOT: Effective Design of Biological Sequences with Limited Experimental Datacited by 1
  9. 2026Repurposing Protein Language Models for Latent Flow-Based Fitness Optimizationcited by 0
  10. 2026From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactionscited by 0
  11. 2026Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgetscited by 0
  12. 2026Feedback-Calibrated Protein Optimization with Batch-Aligned Tail Arbitrationcited by 0
1 more
  1. 2026Structure-aware Reinforcement Learning for Protein Directed Evolutioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Steering Generative Models with Experimental Data for Protein Fitness Optimization
  2. 2024Reinforcement Learning for Sequence Design Leveraging Protein Language Models
  3. 2025From Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?
  4. 2025Evolutionary Profiles for Protein Fitness Prediction
  5. 2026Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
  6. 2025Sparse Autoencoders for Low-N Protein Function Prediction and Design
  7. 2025Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation
  8. 2024Model-based reinforcement learning for protein backbone design