Natural-Language-Guided Generator-Agnostic Shortlisting for Protein Binder Design
研究使用LLM从预计算的结构置信度和界面质量代理分数生成多指标排序策略，以解决蛋白质结合剂设计中候选者筛选的问题。
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Progressive Multi-Agent Reasoning for Biological Perturbation Prediction
This work addresses the challenge of predicting target gene regulatory responses in bulk-cell populations under complex chemical perturbations, where existing methods struggle to model the causal entanglement among high-dimensional perturbations. The authors propose PBio-Agent, a multi-agent framework that introduces the first benchmark for bulk-perturbation response prediction, termed LINCSQA, and incorporates a causal structure sharing assumption. The framework features difficulty-aware task sequencing and iterative knowledge refinement, with specialized agents enhanced by biological knowledge graphs to enable collaborative reasoning. A synthesis agent integrates predictions while a verification agent ensures logical consistency. Evaluated on LINCSQA and PerturbQA, PBio-Agent significantly outperforms current approaches, substantially improving both performance and interpretability of smaller models in complex biological perturbation prediction tasks.
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Natural-Language-Guided Generator-Agnostic Shortlisting for Protein Binder Design
研究使用LLM从预计算的结构置信度和界面质量代理分数生成多指标排序策略，以解决蛋白质结合剂设计中候选者筛选的问题。
0 citationsRead paper
Progressive Multi-Agent Reasoning for Biological Perturbation Prediction
This work addresses the challenge of predicting target gene regulatory responses in bulk-cell populations under complex chemical perturbations, where existing methods struggle to model the causal entanglement among high-dimensional perturbations. The authors propose PBio-Agent, a multi-agent framework that introduces the first benchmark for bulk-perturbation response prediction, termed LINCSQA, and incorporates a causal structure sharing assumption. The framework features difficulty-aware task sequencing and iterative knowledge refinement, with specialized agents enhanced by biological knowledge graphs to enable collaborative reasoning. A synthesis agent integrates predictions while a verification agent ensures logical consistency. Evaluated on LINCSQA and PerturbQA, PBio-Agent significantly outperforms current approaches, substantially improving both performance and interpretability of smaller models in complex biological perturbation prediction tasks.
0 citationsRead paper