做材料发现和生物医学交叉研究的团队终于有了一个可落地的AI原生平台蓝图——它解决了数据碎片化和治理缺失的痛点，做纳米药物递送或生物材料设计的可以直接参考其试点方案。
AIMBio-Mat 是一个概念框架，旨在将材料科学和生物医学数据整合到一个AI原生的、符合FAIR原则（可查找、可访问、可互操作、可重用）且具备治理意识的决策层中。该框架通过知识图谱、不确定性感知机器学习和人机协同主动学习，将生物医学材料发现建模为不确定性下的约束多目标优化问题。它提出了元数据、模型文档、风险分级治理和评估指标等实用要求，并包含最小可行原型规范和用于药物递送的纳米材料AI引导发现试点。该平台定位为探索性和临床前发现基础设施，而非临床决策支持软件，其核心贡献是将碎片化的材料和生物医学记录转化为可审计、可实验操作且负责任的发现工作流蓝图。
AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation
Materials discovery and biomedical translation increasingly require models that can reason across composition, processing, structure, biological response, manufacturability, safety, and governance constraints. Existing materials and biomedical data ecosystems are powerful but remain poorly coupled for AI-guided discovery. Here we present AIMBio, a conceptual framework for an AI-native, FAIR, and governance-aware decision layer that links materials provenance, biomedical context, knowledge graphs, uncertainty-aware machine learning, and human-in-the-loop active learning. The framework formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty and introduces practical requirements for metadata, model documentation, risk-tiered governance, evaluation metrics, and phased implementation. To make the roadmap testable, we add a minimum viable prototype specification and a worked pilot for AI-guided nanomaterials for drug delivery. AIMBio is positioned as exploratory and preclinical discovery infrastructure, not as clinical decision-support software; any clinical or regulated-device use would require separate validation, change control, and regulatory review. The central contribution is a publishable platform blueprint for converting fragmented materials and biomedical records into auditable, experimentally actionable, and translationally responsible discovery workflows.