Abstract
Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.
| Original language | English |
|---|---|
| Pages (from-to) | 367-384 |
| Number of pages | 18 |
| Journal | Journal of Computer-Aided Molecular Design |
| Volume | 22 |
| Issue number | 6-7 |
| DOIs | |
| State | Published - Jun 1 2008 |
| Externally published | Yes |
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Keywords
- Animal toxicity
- Cell proliferation
- Domain applicability
- HT Sassay
- Jurkat cell line
- QSAR
ASJC Scopus subject areas
- Molecular Medicine
Cite this
Utilizing high throughput screening data for predictive toxicology models : Protocols and application to MLSCN assays. / Guha, Rajarshi; Schuerer, Stephan C.
In: Journal of Computer-Aided Molecular Design, Vol. 22, No. 6-7, 01.06.2008, p. 367-384.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Utilizing high throughput screening data for predictive toxicology models
T2 - Protocols and application to MLSCN assays
AU - Guha, Rajarshi
AU - Schuerer, Stephan C
PY - 2008/6/1
Y1 - 2008/6/1
N2 - Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.
AB - Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.
KW - Animal toxicity
KW - Cell proliferation
KW - Domain applicability
KW - HT Sassay
KW - Jurkat cell line
KW - QSAR
UR - http://www.scopus.com/inward/record.url?scp=44249108095&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=44249108095&partnerID=8YFLogxK
U2 - 10.1007/s10822-008-9192-9
DO - 10.1007/s10822-008-9192-9
M3 - Article
VL - 22
SP - 367
EP - 384
JO - Journal of Computer-Aided Molecular Design
JF - Journal of Computer-Aided Molecular Design
SN - 0920-654X
IS - 6-7
ER -