analiza aiqbench
CheMLFlow addresses a critical bottleneck in scientific machine learning by providing an open-source framework for orchestrating end-to-end, reproducible workflows from data curation to model validation. By integrating modular components with agent-assisted experimentation capabilities, the platform streamlines high-throughput research across quantum mechanical and bioactivity prediction tasks without requiring custom orchestration code. This tool directly empowers researchers in materials science and chemistry to standardize their ML pipelines while leveraging emerging AI agents for automated experiment construction and analysis.
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
arXiv:2608.04942v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Other Condensed Matter (cond-mat.other); Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph)
arXiv:2608.04942 [cs.LG]
(or arXiv:2608.04942v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.04942 arXiv-issued DOI via DataCite (pending registration)
Text integral sub licența deschisă a publicației · 213 cuvinte
