用先例引导的AI助手，让药物改写更安全有效
A Precedent-Guided Co-Scientist for Side-Effect-Aware Drug Redesign
- 基于药物-副作用关联与医学知识图谱推理
- 在保留疗效前提下针对性降低特定副作用
- 适合药物研发人员与需要可解释性AI的科研团队
我们提出PRECEDE，一种以先例为导向的协同科学家，用于在保持治疗功能的前提下，针对特定副作用对母体化合物进行重设计。不同于孤立的分子生成，PRECEDE将重设计过程建模为基于药物-副作用关联、生物医学知识图谱以及安全优化先例的证据驱动推理，由大语言模型协调，具备明确策略和人工审核节点。PRECEDE被定位为一种人机协作的AI科研流程，确保假设可审计、可验证，并受既有药理学约束。
原文摘要 · Abstract (English)
We propose PRECEDE, a precedent-guided co-scientist for side-effect-aware drug redesign that revises a parent compound to mitigate a specified side effect while preserving therapeutic function. Rather than isolated molecular generation, PRECEDE frames redesign as evidence-grounded reasoning over drug--side-effect associations, biomedical knowledge graphs, and precedents of safety-driven optimization, coordinated by an LLM orchestrator with explicit policies and human-review checkpoints. We position PRECEDE as a human-supervised AI-for-science workflow in which hypotheses remain auditable, falsifiable, and bounded by prior pharmacology.
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