Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
arXiv:2605.26690v1 Announce Type: new Abstract: Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informative. Existing reinforcement learning and off-policy generative approaches often degrade under surrogate noise, and position-agnostic mutation proposals risk disrupting functionally critical residues. We introduce SILO, a trajectory-level self-improvement imitation framework for oracle-budgeted protein design. SILO uses a hierarchical edit policy that decomposes each mutation into a position choice f
The increasing push for efficient and robust protein design, coupled with limitations of existing AI methods under tight evaluation budgets and noisy data, drives the development of new solutions.
Advanced protein design techniques are critical for synthetic biology applications, drug discovery, and materials science, accelerating innovation in these fields.
The introduction of frameworks like SILO, which leverage self-improvement and biologically guided search, offers a more robust and efficient approach to protein optimization, particularly under practical constraints.
- · Biotechnology sector
- · Pharmaceutical companies
- · AI-driven drug discovery platforms
- · Synthetic biology researchers
- · Traditional high-throughput screening methods
- · Less efficient computational protein design platforms
- · Drug discovery pipelines with long development cycles
Accelerated discovery of novel proteins with enhanced or new functionalities.
Reduced costs and timelines for developing new therapeutics and biomaterials.
Potential for breakthroughs in personalized medicine, bio-manufacturing, and environmental solutions through programmable biology.
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Read at arXiv cs.LG