Background:
Alzheimer's disease swept every corner of the globe and the number of patients
worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease
in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s
drugs has become one of the most popular medical topics.
Methods:
In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute
discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined
with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors.
Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to
construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for
Receiver Operating Characteristic curve(ROC) analysis.
Results:
The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1,
Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively,
which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5.
Conclusion:
In this paper, we conclude that Random Forest is the best learner model for the prediction
of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule
is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish
the target protein of a small molecule.
Erratum: Wang et al., “Reducing Amyloid-Related Alzheimer's Disease Pathogenesis by a Small Molecule Targeting Filamin A”
