arXiv:2605.00182v3 Announce Type: replace Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from large-scale sequences, and discrete diffusion-based protein language models~(\eg, DPLMs) are promising for both understanding and generation. However, existing DPLMs typically rely on masked diffusion that contradicts a simple biological intuition: proteins evolve through accumulated edits, not by emerging from masks. Consequently, these frameworks lack explicit pretraining objectives for subs
The rapid advancement in AI, particularly in generative models and diffusion models, is now being directly applied to complex biological challenges like protein evolution and design. This convergence is driven by the increasing computational power and data availability for biological sequences.
Improving protein generation and understanding evolution is critical for drug discovery, material science, and synthetic biology, potentially unlocking novel solutions for health, energy, and industry. Synthetic biology is a key platform for a future bio-economy.
The ability to generate functional proteins more effectively, without relying on limited evolutionary data, could accelerate the design of new biologics, enzymes, and materials, moving beyond traditional trial-and-error methods.
- · Biopharmaceutical companies
- · Synthetic biology startups
- · Material science industry
- · AI-driven drug discovery platforms
- · Traditional protein engineering methods
- · Companies reliant solely on empirical biological discovery
More efficient discovery and design of novel proteins for therapeutic and industrial applications.
Accelerated development of new drugs, vaccines, and advanced materials with designed functionalities, leading to new markets and products.
Ethical and safety considerations may arise from the ease of generating synthetic biological components, requiring new regulatory frameworks and oversight.
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Read at arXiv cs.LG