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arXiv Machine Learning By Truong-Son Hy

Q-BIOLAT: Binary Latent Protein Fitness Landscapes for QUBO-Based Optimization

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Q-BIOLAT is a framework that converts pretrained protein-language-model embeddings into compact binary codes and trains a quadratic unconstrained binary optimization (QUBO) surrogate with unary and pairwise latent interactions for protein fitness optimization. The study demonstrates that binary encodings with similar predictive accuracy can produce different Hamming neighborhoods, affecting local optima and search trajectories, and shows that PCA followed by per‑coordinate median thresholding yields a more balanced binary space than AE/VAE baselines. Experimental evaluation on GFP and AAV fitness landscapes from ProteinGym confirms that simulated annealing, genetic algorithms, and greedy hill climbing can retrieve high‑percentile variants, with decoded candidates reported via surrogate‑predicted scores.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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