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Ai Bioinformatics

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Research Frontiers in Reinforcement Learning for Protein Engineering

Use of reinforcement learning algorithms to optimize protein sequences for enhanced functional properties and stability.

Latent Protein Landscapes: Navigating Design via Learned Representations
Reward Shaping in Sequence Space: Beyond Fitness Predictions
Multi-Objective Protein Evolution Through Constrained RL Agents
Structural Stability as Emergent Constraint in Directed Evolution
Transfer Learning Across Protein Families via RL Curricula
Off-Policy Exploration in High-Dimensional Folding Landscapes
Thermodynamic Uncertainty Relations in Protein Design RL
Inverse Folding as RL State Abstraction for Engineering
Epistatic Interaction Discovery Through Sequential Decision Making
Generative Models as World Models for Protein Optimization

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