CheMLFlow：用于化学信息学与材料信息学应用的开源平台
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
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中文总结 AI 辅助
CheMLFlow是用于构建端到端高通量智能体工作流的开源平台，可降低科学机器学习开发的编排开销，其在多类性质预测任务上达到文献性能，还支持非分子化学数据集的应用。
中文摘要 AI 辅助
CheMLFlow是一个开源平台，用于构建和执行面向科技应用的端到端、高通量且具备智能体（agentic）能力的工作流。该平台针对科学机器学习开发中的常见瓶颈：研究人员常需将数据获取、整理、表示、模型训练、验证、筛选、解释及报告整合为可复现的流水线，即便其核心研究贡献仅涉及其中某一阶段。CheMLFlow提供模块化工作流组件、可直接运行的参考流水线、标准化产物及评估输出，以降低编排开销并支持跨方法与数据集的基准测试。该平台设计具备可扩展性、可复现性及自动化友好特性，包含可插拔的表示与模型、确定性划分、明确的运行产物、批量执行及报告生成功能。随着科学软件日益向智能体辅助实验方向发展，CheMLFlow的配置驱动工作流与结构化输出也为编码智能体提供了实用接口，支持用户在人工监督下构建实验、检查结果并总结发现。本文描述了该系统架构、核心工作流及基准测试结果，其在量子力学、物理化学及生物活性性质预测方面达到文献性能，还展示了涉及时间序列数据集的用例，证明其应用范围超出分子化学数据集。
英文摘要
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.
