Journal
MOLECULES
Volume 21, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/molecules21080983
Keywords
biological data; drug discovery; virtual screening; prediction of biological activity
Funding
- Ministry of Higher Education (MOHE)
- Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM) under the Research University Grant Category [VOT Q.J130000.2528.14H75]
Ask authors/readers for more resources
Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naive Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available