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Research Paper#Protein Design / AI in Biology🔬 ResearchAnalyzed: Jan 3, 2026 15:47

SeedProteo: AI for Protein Binder Design

Published:Dec 30, 2025 12:50
1 min read
ArXiv

Analysis

This paper introduces SeedProteo, a diffusion-based AI model for designing protein binders. It's significant because it leverages a cutting-edge folding architecture and self-conditioning to achieve state-of-the-art performance in both unconditional protein generation (demonstrating length generalization and structural diversity) and binder design (achieving high in-silico success rates, structural diversity, and novelty). This has implications for drug discovery and protein engineering.
Reference

“SeedProteo achieves state-of-the-art performance among open-source methods, attaining the highest in-silico design success rates, structural diversity and novelty.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Dec 25, 2025 12:01

PLAID: Generating Proteins with Latent Diffusion and Protein Folding Models

Published:Apr 8, 2025 10:30
1 min read
Berkeley AI

Analysis

This article introduces PLAID, a novel multimodal generative model that leverages the latent space of protein folding models to simultaneously generate protein sequences and 3D structures. The key innovation lies in addressing the multimodal co-generation problem, which involves generating both discrete sequence data and continuous structural coordinates. This approach overcomes limitations of previous models, such as the inability to generate all-atom structures directly. The model's ability to accept compositional function and organism prompts, coupled with its trainability on large sequence databases, positions it as a promising tool for real-world applications like drug design. The article highlights the importance of moving beyond structure prediction towards practical applications.
Reference

“In PLAID, we develop a method that learns to sample from the latent space of protein folding models to generate new proteins.”

PermalinkBerkeley AI