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

arXiv: https://arxiv.org/abs/2405.18986 · HTML: https://mlsanity.com/paper/2405.18986/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [Biological Sequence Design with GFlowNets](https://mlsanity.com/paper/2203.04115/) (2022)
- [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)
- [Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2205.15019/) (2022)
- [Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval](https://mlsanity.com/paper/2205.13760/) (2022)
- [AdaLead: A simple and robust adaptive greedy search algorithm for sequence design](https://mlsanity.com/paper/2010.02141/) (2020)
- [Protein Discovery with Discrete Walk-Jump Sampling](https://mlsanity.com/paper/2306.12360/) (2023)
- [Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences](https://mlsanity.com/paper/2306.03111/) (2023)
- [Improving Protein Optimization with Smoothed Fitness Landscapes](https://mlsanity.com/paper/2307.00494/) (2023)
- [[CLS] Token is All You Need for Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2304.06212/) (2023)
- [ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins](https://mlsanity.com/paper/2201.09948/) (2022)

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

- [Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling](https://mlsanity.com/paper/2504.10612/) (2025)
- [Improved Off-policy Reinforcement Learning in Biological Sequence Design](https://mlsanity.com/paper/2410.04461/) (2024)
- [Steering Protein Language Models](https://mlsanity.com/paper/2509.07983/) (2025)
- [Regulatory DNA sequence Design with Reinforcement Learning](https://mlsanity.com/paper/2503.07981/) (2025)
- [PDFBench: A Benchmark for De novo Protein Design from Function](https://mlsanity.com/paper/2505.20346/) (2025)
- [A Variational Perspective on Generative Protein Fitness Optimization](https://mlsanity.com/paper/2501.19200/) (2025)
- [ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods](https://mlsanity.com/paper/2505.22494/) (2025)
- [GROOT: Effective Design of Biological Sequences with Limited Experimental Data](https://mlsanity.com/paper/2411.11265/) (2024)
- [Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization](https://mlsanity.com/paper/2602.02425/) (2026)
- [From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions](https://mlsanity.com/paper/2603.18505/) (2026)
- [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)
- [Structure-aware Reinforcement Learning for Protein Directed Evolution](https://mlsanity.com/paper/2609.39048/) (2026)

## 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.