Artificial Intelligence for chemical risk assessment

Wittwehr, Clemens; Blomstedt, Paul; Gosling, John Paul; Peltola, Tomi; Raffael, Barbara; Richarz, Andrea-Nicole; Sienkiewicz, Marta; Whaley, Paul; Worth, Andrew; Whelan, Maurice

doi:10.1016/j.comtox.2019.100114

Cited by 32 publications

(21 citation statements)

References 15 publications

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“…The effect prediction using TERA is also in line with a larger shift in ecological risk assessment towards the use of artificial intelligence [80]. We also believe the development of TERA contributes to a methodological change in the community, and encourages others to make their data interoperable.…”

Section: Value For the Ecotoxicology Communitysupporting

confidence: 55%

Prediction of adverse biological effects of chemicals using knowledge graph embeddings

Myklebust

Jiménez-Ruiz

Chen

et al. 2022

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We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.

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Section: Value For the Ecotoxicology Communitysupporting

confidence: 55%

Prediction of adverse biological effects of chemicals using knowledge graph embeddings

Myklebust

Jiménez-Ruiz

Chen

et al. 2022

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“…Natural Language Processing (NLP) techniques, including Named Entity Recognition for tagging entities and sentiment analysis for identifying relationships, will be central to automation (Marshall and Wallace 2019;O'Connor et al 2020). Various other machine learning applications could drastically reduce the time needed to review and vet evidence (Wittwehr et al 2020). The use of semantic authoring tools that would render new studies machine-readable (Eldesouky et al 2016;Oliveira et al 2017;Oldman and Tanase 2018) would obviate many of the challenges in annotating research documents and should be explored for toxicology and environmental health contexts.…”

Section: Escaping the Streetlightmentioning

confidence: 99%

“…2020 ). Various other machine learning applications could drastically reduce the time needed to review and vet evidence ( Wittwehr et al. 2020 ).…”

Section: Building An Ontologized Kosmentioning

confidence: 99%

Knowledge Organization Systems for Systematic Chemical Assessments

Whaley

Edwards

Kraft

et al. 2020

Environ Health Perspect

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Background: Although the implementation of systematic review and evidence mapping methods stands to improve the transparency and accuracy of chemical assessments, they also accentuate the challenges that assessors face in ensuring they have located and included all the evidence that is relevant to evaluating the potential health effects an exposure might be causing. This challenge of information retrieval can be characterized in terms of “semantic” and “conceptual” factors that render chemical assessments vulnerable to the streetlight effect. Objectives: This commentary presents how controlled vocabularies, thesauruses, and ontologies contribute to overcoming the streetlight effect in information retrieval, making up the key components of Knowledge Organization Systems (KOSs) that enable more systematic access to assessment-relevant information than is currently achievable. The concept of Adverse Outcome Pathways is used to illustrate what a general KOS for use in chemical assessment could look like. Discussion: Ontologies are an underexploited element of effective knowledge organization in the environmental health sciences. Agreeing on and implementing ontologies in chemical assessment is a complex but tractable process with four fundamental steps. Successful implementation of ontologies would not only make currently fragmented information about health risks from chemical exposures vastly more accessible, it could ultimately enable computational methods for chemical assessment that can take advantage of the full richness of data described in natural language in primary studies. https://doi.org/10.1289/EHP6994

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“…AI-based toxicity prediction models have the potential to address the limitations of animal testing and develop alternative testing methods. − Currently, efforts to improve the predictive performance of models using various algorithms including supervised, unsupervised, consensus models with various molecular descriptors are getting quite successful results. However, several issues remain regarding the use of AI models in chemical management.…”

Section: Application Of Ai-based Toxicity Prediction In Chemical Mana...mentioning

confidence: 99%

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Jeong

Choi

2022

Environ. Sci. Technol.

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Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure–activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

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Artificial Intelligence for chemical risk assessment

Cited by 32 publications

References 15 publications

Prediction of adverse biological effects of chemicals using knowledge graph embeddings

Prediction of adverse biological effects of chemicals using knowledge graph embeddings

Knowledge Organization Systems for Systematic Chemical Assessments

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

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