Aug 2026· Journal of Chemical Information and Modeling· 0 citations· 26 references
Abstract
We introduce ChemLint, an open-source Model Context Protocol (MCP) server that connects any MCP-compatible large language model to a curated suite of local cheminformatics and machine learning tools, enabling rigorous molecular data handling through a conversational interface. Molecular machine learning studies are frequently undermined by inconsistent data preprocessing, including invalid SMILES, unresolved duplicates, and train-test leakage, yet existing large language model (LLM)-based chemistry tools do not address these data-centric challenges. ChemLint provides tools for data exploration and diagnostics, molecule standardization, and machine learning modeling. All operations are executed deterministically by established libraries and recorded in a project manifest that traces every action, supporting reproducibility and making curation choices explicit. We demonstrate through several examples how ChemLint can be used to identify common data quality issues, evaluate splitting strategies, and execute complete modeling pipelines from raw data to evaluation. ChemLint is freely available at https://github.com/molML/ChemLint.
The proposed Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists, achieves state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5.
Weida Wang, Benteng Chen, Di Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
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 are described.
Brendan Smith, S. López-Moreno, E. Dolores-Cuenca et al.· 0 citations
Training set quality and diversity are key determinants of the reliability of machine learning interatomic potentials (MLIPs), yet using massive datasets in full is often impractical and redundant, making intelligent data selection essential. A major bottleneck, however, is the lack of infrastructure for uniformly acce...
M. Bluntzer, Jules Tilly, Christoph Brunken· 0 citations
This tutorial provides a comprehensive, end-to-end workflow from raw data to deployed models,icitly designed for environmental chemists with limited prior experience in ML modeling while also providing practical guidance for other users seeking to strengthen their modeling workflows.
Kai Zhang, Yu-Shu Cheng, Hai-Ping Ai et al.· ACS Environmental Au· 0 citations
This work introduces ChemDIRT (Diversified Instruction, Representation, and Task Benchmark), a comprehensive evaluation framework designed to assess the robustness of chemical reasoning in LLMs and benchmark a diverse set of open- and closed-source LLMs.
Eric Inae, Tim Gunn, Chris Bond et al.· 0 citations
Transformer models have revolutionized natural language processing (NLP), and text-based molecular representations like SMILES have successfully extended these architectures to chemistry. However, domain-adaptive pre-training often causes models to overfit to chemical syntax, catastrophically forgetting their foundatio...
David Ming Segura, Jeremy Goumaz, Joshua W. Sin et al.· 1 citation
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