The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Autoqsar: An Automated Machine Learning Tool for Best-Practice Quantitative Structure–Activity Relationship Modeling
Abstract
AIM: We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. METHODOLOGY/RESULTS: The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. CONCLUSION: AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.
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Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164
Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Robinson, S., Dahlgren, M. K., Greenwood, J., et al. (2015) · Journal of the American Chemical Society · doi:10.1021/ja512751q · Cited by 1,313
Self-citationComputer‐Assisted Synthetic Planning: The End of the Beginning
Szymkuć, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., & Grzybowski, B. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201506101 · Cited by 611
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Sadybekov, A. V., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,279
Advancing drug discovery via artificial intelligence
Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.004 · Cited by 654
The transformational role of GPU computing and deep learning in drug discovery
Pandey, M., Fernandez, M., Gentile, F., Isayev, O., Tropsha, A., Stern, A. C., & Cherkasov, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00463-x · Cited by 255
Towards reproducible computational drug discovery
Schaduangrat, N., Lampa, S., Simeon, S., Gleeson, M. P., Spjuth, O., & Nantasenamat, C. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0408-x · Cited by 208
Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00516-0 · Cited by 78
“To achieve this, they trained an AutoQSAR model [49] on a subset of 1,000 enumerated analogues of a potent inhibitor with the corresponding FEP predictions, which was subsequently used as the REINVENT scoring function.”
Method
Metadata
- Authors
- Dixon, S. L., Duan, J., Smith, E., Von Bargen, C. D., Sherman, W., & Repasky, M. P.
- Venue
- Future Medicinal Chemistry
- Publisher
- Informa UK Limited
- Published
- 2016-09-19
- Volume
- 8
- Issue
- 15
- Pages
- 1825-1839
- ISSN
- 1756-8919 (ISSN-L)
- 1756-8927
- OpenAlex
- W2525356667
- Cited by
- 183
- References
- 4
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dixon, S. L., Duan, J., Smith, E., Von Bargen, C. D., Sherman, W., & Repasky, M. P. (2016). Autoqsar: An Automated Machine Learning Tool for Best-Practice Quantitative Structure–Activity Relationship Modeling. Future Medicinal Chemistry. https://doi.org/10.4155/fmc-2016-0093
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