Nat. Biomed. Eng. | AI生物学家XunZi发现可改变疾病进程的治疗靶点

TL;DR - XunZi is an "AI biologist" published in Nature Biomedical Engineering that couples an LLM-based mechanistic reasoning module with a graph-based multi-omics fusion module to rank disease-modifying targets, and its predictions were validated in cell and mouse experiments for non-small cell lung cancer and Parkinson's disease.

• Architecture: XunZi-R is a 7.3B-parameter open LLM continually pretrained on ~24.4M biomedical papers, >2M structured biology corpora, and >330K human-corrected gene–disease mechanism explanations; XunZi-M is a graph convolutional network over ~620K nodes / 6.09M edges (~2.81M protein interactions, >47K biological process annotations) fused with ~613 TB of transcriptomic, proteomic, and phosphoproteomic data. Scores from both are fused for target ranking.
• NSCLC: of 20 top-ranked previously unreported candidates, knockdown of MYO1B, NAA30, BRCC3, GFPT1, and PGAM5 reduced A549 viability (vs. 1/20 for genes picked by differential expression). MYO1B knockdown lowered AKT and ERK phosphorylation, matching the model's predicted PI3K–AKT / MAPK–ERK mechanism; effects were weaker in small-cell lung cancer and liver cancer cells, suggesting context specificity.
• Parkinson's: CHK2, IRAK4, and STK33 emerged as candidate pathogenic kinases. Chk2 was hyperactivated in substantia nigra (not cerebellum) in both MPTP and α-synuclein PFF models; AAV knockdown or the selective inhibitor CCT241533 improved pole/rotarod performance, restored tyrosine hydroxylase, reduced dopaminergic neuron loss, and lowered p53 activation. Chk2 inhibition also reduced LRRK2 activation, with a detected CHK2–LRRK2 interaction.
• Limitations acknowledged: unstudied gene–disease pairs are treated as negatives, human curation may inject bias, rare-disease generalization is unproven, direct CHK2→LRRK2 phosphorylation is unconfirmed, and mouse efficacy plus CCT241533 safety/PK do not translate directly to humans.