ArticleMolecules (Basel, Switzerland)2024
Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.
Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification of new ER-targeted anticancer hits active against MCF-7 breast cancer cells in the marine fungal secondary metabolite library using computative and experimental approaches.Journal of computer-aided molecular design · 2026Article
- Biopesticidal Potential of theInternational journal of molecular sciences · 2026Article
- Antitarget profiling of mycotoxins via molecular docking.Molecular diversity · 2026Article
- Towards Explainable Computational Toxicology: Linking Antitargets to Rodent Acute Toxicity.Pharmaceutics · 2025Article
- Analysis of the Toxicological Profile ofPlants (Basel, Switzerland) · 2025Article
- Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles.Journal of computer-aided molecular design · 2025Article
- Development and Prospects of Furin Inhibitors for Therapeutic Applications.International journal of molecular sciences · 2024Review
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Authors and funding
9 authors.
Funding
Abstract
In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.
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Registered trials
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