Sequence Optimization

Sequence optimization aims to identify optimal sequences—be they protein sequences, DNA codons, or operational sequences in computing—that maximize a desired outcome, such as protein expression, computational efficiency, or radiotherapy treatment efficacy. Current research employs diverse approaches, including genetic algorithms, reinforcement learning (particularly multi-agent RL), and deep learning models like transformers and neural message passing networks, often coupled with Bayesian optimization techniques. These advancements are significantly impacting fields ranging from protein engineering and synthetic biology to in-memory computing and radiotherapy planning, enabling more efficient design and optimization of complex systems.