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

By Robert P. Sheridan, Wei Min Wang, Andy Liaw, Junshui Ma, Eric M. Gifford. Journal of Chemical Information and Modeling, 2016

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Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, background): “05) than the default value of the implementation, a maximal tree depth of 6, and a larger number of trees (700), as described by Sheridan and co-workers [24].”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “While there is some controversy as to whether the latter type of models are superior performance-wise to other approaches ( e. g . gradient boosting machines [28]) when using the same set of descriptors [29], deep learning methods offer several advantages.”
A data-driven statistical model for predicting the critical temperature of a superconductor (2018, methodology): “Outside the competition realm, XGBoost has been successfully applied in disease prediction by Chen et al. (2018b), and in quantitative structure activity relationships studies by Sheridan et al. (2016).”