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New AI Model ProtLiD^2 Enhances Protein Design with Ligand Conditioning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ProtLiD$^2$, a novel discrete diffusion model designed for protein sequence-structure co-design that explicitly incorporates ligand information. This model generates both amino acid sequences and discrete structure tokens, leveraging geometry-aware cross-attention to integrate ligand chemical and geometric data. Trained on over a million ligand-protein complexes, ProtLiD$^2$ demonstrates significant improvements in protein design metrics, including global fold confidence and active-site accuracy, outperforming existing methods like Complexa, FAIR, and PocketGen. AI

IMPACT This model advances AI's capability in scientific discovery, potentially accelerating drug development and protein engineering.

RANK_REASON The cluster contains a research paper detailing a new AI model for a scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI Model ProtLiD^2 Enhances Protein Design with Ligand Conditioning

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The cluster contains a research paper detailing a new AI model for a scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chen Wei, Fanding Xu, Minghao Sun, Zhiyuan Liu, Lin Wang, Tianrui Jia, Yihang Zhou, Yang Zhang ·

    Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

    arXiv:2605.27413v1 Announce Type: cross Abstract: Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under expl…

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