ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees
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Abstract (arXiv)
The global decline in bee populations poses significant risks to agriculture, biodiversity, and environmental stability. To bridge the gap in existing data, we introduce ApisTox, a comprehensive dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and cheminformatics. This makes it a valuable tool for both academic research and practical applications in bee conservation.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 2 of its 78 references that are in the map. First posted on arXiv 24 Apr 2024.
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2 of 78 references.
References · 2
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Citing papers per year: 2024: 1; 2025: 3; 2026: 2.
6 papers.
- 2024Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Pythoncited by 9
- 2025Evaluating machine learning models for predicting pesticide toxicity to honey beescited by 2
- 2025MolPILE -- large-scale, diverse dataset for molecular representation learningcited by 1
- 2025Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicologycited by 1
- 2026Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformaticscited by 0
- 2026Benchmarking graph-based models for in-silico toxicity prediction in drug discoverycited by 0
- 2026Benchmarking graph-based models for in-silico toxicity prediction in drug discovery29 Sep 2026
- 2026Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformatics3 Aug 2026
- 2025Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology23 Sep 2025
- 2025MolPILE -- large-scale, diverse dataset for molecular representation learning22 Sep 2025
- 2025Evaluating machine learning models for predicting pesticide toxicity to honey bees31 Mar 2025
- 2024Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python18 Jul 2024