Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes
Abstract
The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.
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Liu et al. established a variable-nearest neighbor model to predict QSAR between chemical structures and gene expression profiles, obtaining AUC >0.7.
“Liu et al. established a variable-nearest neighbor model to predict the QSAR between chemical structures and gene expression profiles, and obtained an AUC value of more than 0.7 [139].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [139]
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Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
- Authors
- Liu, R., AbdulHameed, M. D. M., & Wallqvist, A.
- Affiliations
- Telemedicine & Advanced Technology Research Center (US)
- Corresponding author
- Liu, Ruifeng
- Wallqvist, Anders
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-08-22
- Volume
- 57
- Issue
- 9
- Pages
- 2194-2202
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2744616328
- Cited by
- 7
- References
- 20
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 1 · 2018: 2 · 2016: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research and Materiel Command
- Defense Threat Reduction Agency (CBCall14-CBS-05-2-0007)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- QSAR, drug-induced liver injury, drug-induced nephrotoxicity, drug-induced cardiotoxicity, drug safety prediction, structure-activity relationship
- MeSH terms
- Machine Learning, Gene Expression Regulation, Humans, Computational Biology, Quantitative Structure-Activity Relationship
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