Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity

Liu, Ruifeng; Madore, Michael; Glover, Kyle P; Feasel, Michael G; Wallqvist, Anders

doi:10.1093/toxsci/kfy111

Cited by 47Computational Drug Discovery MethodsMachine Learning in Materials ScienceAnimal testing and alternatives

Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity

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Liu, Ruifeng, Madore, Michael, Glover, Kyle P, Feasel, Michael G, Wallqvist, Anders·2018·Toxicological Sciences

DOI 10.1093/toxsci/kfy111

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Abstract

Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30 000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

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Liu et al. found that RF and DNN performed better on the global dataset, while k-NN performed better on the unbalanced acute toxicity datasets, using ECFP uniformly.
“It was found that RF and DNN performed better on the global dataset, while k-NN performed better on the unbalanced acute toxicity datasets. This result also highlights the importance of neighbor information in acute toxicity prediction [126].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [126]

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Authors: Liu, R., Madore, M., Glover, K. P., Feasel, M. G., & Wallqvist, A.
Affiliations: Telemedicine & Advanced Technology Research Center (US)
Defense Threat Reduction Agency (US)
U.S. Army Combat Capabilities Development Command Chemical Biological Center (US)
Corresponding author: Liu, Ruifeng
Wallqvist, Anders
ORCID: Liu, Ruifeng
Feasel, Michael G
Wallqvist, Anders
Venue: Toxicological Sciences
Publisher: Oxford University Press (OUP)
Published: 2018-05-02
Volume: 164
Issue: 2
Pages: 512-526
ISSN: 1096-0929 (ISSN-L)
1096-6080
DOI: 10.1093/toxsci/kfy111
OpenAlex: W2802630768
Cited by: 47
References: 20
Citations by year: 2026: 1 · 2025: 4 · 2024: 5 · 2023: 10 · 2022: 4 · 2021: 6 · 2020: 8 · 2019: 6 · 2018: 3
FWCI: 4.29
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: academic.oup.com/journals/pages/about_us/legal/notices (vor)
Locations: Toxicological Sciences (publishedVersion)
PubMed (publishedVersion)
Funders: U.S. Army Medical Research
Defense Threat Reduction Agency (CBCall14-CBS-05-2-0007)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
Keywords: deep neural network, random forest, QSAR, in silico toxicity prediction, alternatives to animal testing
MeSH terms: Machine Learning, Deep Learning, Animals, Rabbits, Neural Networks, Computer, Toxicity Tests, Computational Biology, Quantitative Structure-Activity Relationship, Mice, Rats, Datasets as Topic

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Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity

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