CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications

2026-08-05

AI Summary

This work addresses the fragmented and labor-intensive pipeline in scientific machine learning—from data acquisition to model deployment—by proposing an end-to-end, high-throughput, and agent-collaborative reproducible workflow platform. The platform enables full automation of data collection, processing, modeling, validation, selection, and reporting through modular components, configuration-driven mechanisms, and standardized artifacts. Key technical features include plug-and-play model architectures, deterministic data splitting, batch execution, and structured outputs. Evaluated on quantum mechanics, physicochemical property, and bioactivity prediction tasks, the system achieves state-of-the-art performance and successfully generalizes to non-molecular domains such as time series, significantly enhancing research efficiency and reproducibility.

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.

Problem

• scientific machine learning
• reproducible pipeline
• workflow orchestration
• high-throughput experimentation
• end-to-end workflow

Innovation

• workflow orchestration
• modular cheminformatics
• reproducible machine learning
• agent-assisted experimentation
• high-throughput screening