10.1021/acs.jcim.9b00633.s001
Jin Zhang
Jin
Zhang
Daniel Mucs
Daniel
Mucs
Ulf Norinder
Ulf
Norinder
Fredrik Svensson
Fredrik
Svensson
LightGBM: An Effective and Scalable Algorithm for
Prediction of Chemical Toxicity–Application to the Tox21 and
Mutagenicity Data Sets
American Chemical Society
2019
LightGBM
mutagenicity data sets
silico safety assessment
leaf-wise tree growth strategy
compound libraries
Tox 21
support vector machines
Mutagenicity Data Sets Machine
scalable algorithm offering
2019-10-09 17:03:19
Journal contribution
https://acs.figshare.com/articles/journal_contribution/LightGBM_An_Effective_and_Scalable_Algorithm_for_Prediction_of_Chemical_Toxicity_Application_to_the_Tox21_and_Mutagenicity_Data_Sets/9960116
Machine learning algorithms have
attained widespread use in assessing
the potential toxicities of pharmaceuticals and industrial chemicals
because of their faster speed and lower cost compared to experimental
bioassays. Gradient boosting is an effective algorithm that often
achieves high predictivity, but historically the relative long computational
time limited its applications in predicting large compound libraries
or developing <i>in silico</i> predictive models that require
frequent retraining. LightGBM, a recent improvement of the gradient
boosting algorithm, inherited its high predictivity but resolved its
scalability and long computational time by adopting a leaf-wise tree
growth strategy and introducing novel techniques. In this study, we
compared the predictive performance and the computational time of
LightGBM to deep neural networks, random forests, support vector machines,
and XGBoost. All algorithms were rigorously evaluated on publicly
available Tox21 and mutagenicity data sets using a Bayesian optimization
integrated nested 10-fold cross-validation scheme that performs hyperparameter
optimization while examining model generalizability and transferability
to new data. The evaluation results demonstrated that LightGBM is
an effective and highly scalable algorithm offering the best predictive
performance while consuming significantly shorter computational time
than the other investigated algorithms across all Tox21 and mutagenicity
data sets. We recommend LightGBM for applications of <i>in silico</i> safety assessment and also other areas of cheminformatics to fulfill
the ever-growing demand for accurate and rapid prediction of various
toxicity or activity related end points of large compound libraries
present in the pharmaceutical and chemical industry.