做科学 ML 的人可以看看这个，它把数据、训练、筛选、报告都串成现成管线，省去自己拼装的麻烦，还自带基准结果。
CheMLFlow 是一个开源平台，用于构建和执行端到端、高通量及智能体驱动的科学工作流。它提供模块化组件、可运行参考管线、标准化产物和评估输出，降低多步骤机器学习管线的编排成本。该平台支持可插拔表示与模型、确定性数据划分、批处理执行和报告生成，并面向智能体辅助实验设计提供了配置驱动接口。论文展示了在量子力学、物理化学和生物活性性质预测基准上达到文献水平的结果，并给出时间序列数据集上超越分子化学的应用案例。
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage. CheMLFlow provides modular workflow components, ready-to-run reference pipelines, standardized artifacts, and evaluation outputs that reduce orchestration overhead and support benchmarking across methods and datasets. The platform is designed to be extensible, reproducible, and automation friendly, with pluggable representations and models, deterministic splits, explicit run artifacts, batch execution, and report generation. As scientific software increasingly moves toward agent assisted experimentation, CheMLFlow's configuration driven workflows and structured outputs also provide a practical interface for coding agents to help users construct experiments, inspect results, and summarize findings under human supervision. This article describes the system architecture, core workflows, and benchmarks that reach literature performance for quantum mechanical, physicochemical and bioactivity property prediction, and use cases involving time series datasets demonstrating applications beyond molecular chemistry datasets.