Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information Article Swipe
Jochen P. Zubrod
,
Nika Galić
,
Maxime Vaugeois
,
David A. Dreier
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.envint.2024.108607
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.envint.2024.108607
Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models' applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support "Green Chemistry" and "Green Toxicology".
Related Topics
Concepts
Machine learning
Applicability domain
Computer science
Multicollinearity
Artificial intelligence
Cheminformatics
Boosting (machine learning)
Kriging
Biological data
Gaussian process
Random forest
Support vector machine
Quantitative structure–activity relationship
Regression analysis
Gaussian
Quantum mechanics
Physics
Biology
Computational chemistry
Chemistry
Genetics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.envint.2024.108607
- OA Status
- gold
- Cited By
- 19
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393948557
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393948557Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.envint.2024.108607Digital Object Identifier
- Title
-
Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological informationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-01Full publication date if available
- Authors
-
Jochen P. Zubrod, Nika Galić, Maxime Vaugeois, David A. DreierList of authors in order
- Landing page
-
https://doi.org/10.1016/j.envint.2024.108607Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.envint.2024.108607Direct OA link when available
- Concepts
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Machine learning, Applicability domain, Computer science, Multicollinearity, Artificial intelligence, Cheminformatics, Boosting (machine learning), Kriging, Biological data, Gaussian process, Random forest, Support vector machine, Quantitative structure–activity relationship, Regression analysis, Gaussian, Quantum mechanics, Physics, Biology, Computational chemistry, Chemistry, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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