Multi-Objective Protein Optimization via Modular Diffusion Steering

Key points

• Protein engineering often requires improving several properties simultaneously, motivating the search for high-performing sequences that represent useful fitness tradeoffs.1
• For each proposal, MODS samples augmented Chebyshev scalarization weights and a fitness-surrogate ensemble member to define a scalar steering reward.1
• We evaluate MODS on six protein optimization tasks using predictive oracles trained on experimental assay data, with two diffusion models and two steering procedures.1
• Our results demonstrate a flexible and effective approach to multi-objective protein optimization with limited feedback.1

Sources (1)

• [1]Multi-Objective Protein Optimization via Modular Diffusion Steering
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:36 PM

  We introduce MODS, a modular framework for multi-objective protein optimization via inference-time diffusion steering.

  Protein engineering often requires improving several properties simultaneously, motivating the search for high-performing sequences that represent useful fitness tradeoffs.

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