Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

Sheridan, Robert P.; Wang, Weimin; Liaw, Andy; Ma, Junshui; Gifford, Eric M.

doi:10.1021/acs.jcim.6b00591

Cited by 387 publications

(259 citation statements)

References 20 publications

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“…We build an individual model for each cell line using the popular Random Forest (RF) algorithm 40 . We also build a second model per cell line using XGBoost (XGB for short) 41 , a recent machine learning method that has helped to win numerous Kaggle competitions 41 as well as to generate highly predictive QSAR models 42 . We validate these models for commonly-encountered prediction scenarios: e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

Sidorov

Naulaerts

Ariey-Bonnet

et al. 2018

Preprint

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Background. Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic.Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Methods. Each cell line is modeled using primarily two machine learning techniques, RandomForest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5000 pair-wise drug combinations, 60 cell lines, 4 types of models and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated.Results. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on 2 the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least two-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models.Conclusions. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

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Section: Introductionmentioning

confidence: 99%

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

Sidorov

Naulaerts

Ariey-Bonnet

et al. 2018

Preprint

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“…XGBoost has been tested in a series of datasets for QSAR modelling, achieving high accuracy and requiring much less computation time than deep neural nets 63 . There are several adjustable parameters in XGBoost.…”

Section: Methodsmentioning

confidence: 99%

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods

Zhang

Chen

et al. 2017

Sci Rep

179

135

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Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models (http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/).

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“…RF can also handle both categorical and continuous variables, which can return the importance of variables and be freely implemented with high quality (Statnikov, Wang, & Aliferis, 2008). In addition, extreme random tree (ERT; Šícho, Kops, Stork, Svozil, & Kirchmair, 2017), AdaBoost (Pérezcastillo et al, 2012), gradient boosting trees (GBT) (Ericksen et al, 2017) and XGBoost (Sheridan, Wei, Liaw, Ma, & Gifford, 2016) models are also widely used in drug design and discovery and achieve favorable outcomes.…”

Section: Introductionmentioning

confidence: 99%

A combined drug discovery strategy based on machine learning and molecular docking

et al. 2019

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Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k‐Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross‐validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure‐based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure‐based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target.

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Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

Cited by 387 publications

References 20 publications

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods

A combined drug discovery strategy based on machine learning and molecular docking

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