Chemical representation learning for toxicity prediction

Born, Jannis; Markert, Greta; Janakarajan, Nikita; Kimber, Talia B.; Volkamer, Andrea; Martínez, María Rodríguez; Manica, Matteo

doi:10.1039/d2dd00099g

Cited by 7 publications

(7 citation statements)

References 100 publications

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“…For example, the linking of GHS hazard statements to respective chemicals (see “Terminology Component”) is essential for automating chemical hazard identification and risk assessment. Moreover, the availability of big data on chemical structures and associated properties can help in predicting potential products’ characteristics, such as toxicology . We demonstrated in “Data Access via Complex Queries”, how OntoSpecies can be used in multiobjective experimental planning tasks such as solvent selection.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the availability of big data on chemical structures and associated properties can help in predicting potential products' characteristics, such as toxicology. 95 We demonstrated in "Data Access via Complex Queries", how OntoSpecies can be used in multiobjective experimental planning tasks such as solvent selection.…”

Section: Case 4: Check Coherence Of Data Reported In Pubchemmentioning

confidence: 99%

See 1 more Smart Citation

Chemical Species Ontology for Data Integration and Knowledge Discovery

Pascazio,

Rihm,

Naseri

et al. 2023

J. Chem. Inf. Model.

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Web ontologies are important tools in modern scientific research because they provide a standardized way to represent and manage web-scale amounts of complex data. In chemistry, a semantic database for chemical species is indispensable for its ability to interrelate and infer relationships, enabling a more precise analysis and prediction of chemical behavior. This paper presents OntoSpecies, a web ontology designed to represent chemical species and their properties. The ontology serves as a core component of The World Avatar knowledge graph chemistry domain and includes a wide range of identifiers, chemical and physical properties, chemical classifications and applications, and spectral information associated with each species. The ontology includes provenance and attribution metadata, ensuring the reliability and traceability of data. Most of the information about chemical species are sourced from PubChem and ChEBI data on the respective compound Web pages using a software agent, making OntoSpecies a comprehensive semantic database of chemical species able to solve novel types of problems in the field. Access to this reliable source of chemical data is provided through a SPARQL end point. The paper presents example use cases to demonstrate the contribution of OntoSpecies in solving complex tasks that require integrated semantically searchable chemical data. The approach presented in this paper represents a significant advancement in the field of chemical data management, offering a powerful tool for representing, navigating, and analyzing chemical information to support scientific research.

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Section: Resultsmentioning

confidence: 99%

Section: Case 4: Check Coherence Of Data Reported In Pubchemmentioning

confidence: 99%

Chemical Species Ontology for Data Integration and Knowledge Discovery

Pascazio,

Rihm,

Naseri

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…In drug discovery, these tools have impacted bioactivity prediction, 1,2 de novo molecular design, 3–8 synthesis prediction 9–15 and molecular property prediction. 16–20 In turn, the demand for high-quality data has increased beyond the extent of existing data sources 21 and there is a need to facilitate a larger number of informative experiments to generate data in a standardized format. Combinatorial chemistry is a popular method for producing large collections of compounds, motivated by material efficiency and more sustainable chemistry 22,23 since synthesis of 100 molecules using two building blocks per synthesis could in the worst case require 200 different building blocks, whereas a library of the same size using combinatorial chemistry would use 20 in a 10 × 10 design.…”

Section: Introductionmentioning

confidence: 99%

De novo generated combinatorial library design

Johansson,

Haghir Chehreghani,

Engkvist

et al. 2024

Digital Discovery

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“…The second of the two environments contains just a single atom, but has five occurrences in the compound. The environment attribution is shared between those five atoms (5,6,7,8,9). To obtain the depiction, the atom attributions obtained from all bits are aggregated.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Typical machine learning methods include k-nearest neighbors, support vector machines, random forest, gradient tree boosting, and more recently deep neural networks (DNN) . Chemical fingerprints are widely used as input features, however, a recent focus has been on the use of different types of descriptors including, for example, string representations such as SMILES, , depictions of chemical structures as inputs to DNNs, and 2D and 3D chemical graphs − which have been used with both classical machine learning methods and with more novel graph-based DNN architectures.…”

Section: Introductionmentioning

confidence: 99%

Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features

Walter,

Webb,

Gillet

2024

J. Chem. Inf. Model.

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Neural network models have become a popular machine-learning technique for the toxicity prediction of chemicals. However, due to their complex structure, it is difficult to understand predictions made by these models which limits confidence. Current techniques to tackle this problem such as SHAP or integrated gradients provide insights by attributing importance to the input features of individual compounds. While these methods have produced promising results in some cases, they do not shed light on how representations of compounds are transformed in hidden layers, which constitute how neural networks learn. We present a novel technique to interpret neural networks which identifies chemical substructures in training data found to be responsible for the activation of hidden neurons. For individual test compounds, the importance of hidden neurons is determined, and the associated substructures are leveraged to explain the model prediction. Using structural alerts for mutagenicity from the Derek Nexus expert system as ground truth, we demonstrate the validity of the approach and show that model explanations are competitive with and complementary to explanations obtained from an established feature attribution method.

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Chemical representation learning for toxicity prediction

Abstract: A chemical language model for molecular property prediction: it outperforms prior art, is validated on a large, proprietary toxicity dataset, reveals cytotoxic motifs through attention & uses two uncertainty techniques to improve model reliability.

Cited by 7 publications

References 100 publications

Chemical Species Ontology for Data Integration and Knowledge Discovery

Chemical Species Ontology for Data Integration and Knowledge Discovery

De novo generated combinatorial library design

Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features

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