Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
Abstract
In the pharmaceutical industry it is common to generate many QSAR models from training sets containing a large number of molecules and a large number of descriptors. The best QSAR methods are those that can generate the most accurate predictions but that are not overly expensive computationally. In this paper we compare eXtreme Gradient Boosting (XGBoost) to random forest and single-task deep neural nets on 30 in-house data sets. While XGBoost has many adjustable parameters, we can define a set of standard parameters at which XGBoost makes predictions, on the average, better than those of random forest and almost as good as those of deep neural nets. The biggest strength of XGBoost is its speed. Whereas efficient use of random forest requires generating each tree in parallel on a cluster, and deep neural nets are usually run on GPUs, XGBoost can be run on a single CPU in less than a third of the wall-clock time of either of the other methods.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Boosting is very useful because it is often one of the most accurate and fastest methods, especially with the latest implementation of Extreme (XGBoost) and Light Gradient Boosting Machine.
“Boosting is also very useful because it is often one of the most accurate and fastest methods, especially with the latest implementation of Extreme (XGBoost61) and Light Gradient Boosting Machine.62”
From QSAR without borders (2020) · cites this paper as [61]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
Svetnik, V., Liaw, A. I., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034160g · Cited by 3,587 · see citing works
Self-citationGraph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works
Self-citationAtom pairs as molecular features in structure-activity studies: definition and applications
Carhart, R. E., Smith, D. H., & Venkataraghavan, R. (1985) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00046a002 · Cited by 1,120 · see citing works
Deep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723 · see citing works
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376 · see citing works
Self-citationChemical Similarity Using Physiochemical Property Descriptors
Kearsley, S. K., Sallamack, S. J., Fluder, E. M., Andose, J. D., Mosley, R. T., & Sheridan, R. P. (1996) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci950274j · Cited by 267 · see citing works
Self-citationBoosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling
Svetnik, V., Wang, T., Tong, C., Liaw, A. I., Sheridan, R. P., & Song, Q. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0500379 · Cited by 219 · see citing works
Self-citationContemporary QSAR Classifiers Compared
Bruce, C. L., Melville, J. L., Pickett, S. D., & Hirst, J. D. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600332j · Cited by 139 · see citing works
Using Random Forest To Model the Domain Applicability of Another Random Forest Model
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400482e · Cited by 127 · see citing works
Self-citationCortés‐Ciriano, I., Bender, A., & Malliavin, T. E. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00101 · Cited by 37 · see citing works
Sheridan, R. P. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500084w · Cited by 24 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Self-citationJiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., & Hou, T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-020-00479-8 · Cited by 664 · see citing works
Artificial intelligence in chemistry and drug design
Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00317-x · Cited by 165 · see citing works
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space
Sosnin, S., Karlov, D., Tetko, I. V., & Fedorov, M. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00685 · Cited by 102 · see citing works
“This result is not surprising, and it confirmed observations of previous studies 48,59 that the efficiency of these methods is similar.”
MethodResultSheridan, R. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00825 · Cited by 84 · see citing works
Self-citationPark, D., Son, S. I., Kim, M. S., Kim, T. Y., Choi, J. H., Lee, S.-E., Hong, D., & Kim, M.-C. (2023) · Scientific Reports · doi:10.1038/s41598-023-34999-8 · Cited by 17 · see citing works
- Authors
- Sheridan, R. P., Wang, W. M., Liaw, A., Ma, J., & Gifford, E. M.
- Affiliations
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- MSD (Singapore) (SG)
- Corresponding author
- Sheridan, Robert P.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-12-13
- Volume
- 56
- Issue
- 12
- Pages
- 2353-2360
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2556851635
- Cited by
- 544
- References
- 17
- Citations by year
- 2026: 63 · 2025: 92 · 2024: 87 · 2023: 71 · 2022: 74 · 2021: 49 · 2020: 48 · 2019: 31 · 2018: 22 · 2017: 7
- FWCI
- 18.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- XGBoost, extreme gradient boosting, QSAR, random forest, deep neural network, computational efficiency, hyperparameter optimization, molecular descriptors
- MeSH terms
- Algorithms, Humans, Models, Biological, Software, Quantitative Structure-Activity Relationship, Drug Discovery, Databases, Pharmaceutical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
