Title:Application of Machine Learning Techniques in Drug-target Interactions Prediction
Volume: 27
Issue: 17
Author(s): Shengli Zhang*, Jiesheng Wang, Zhenhui Lin and Yunyun Liang
Affiliation:
- School of Mathematics and Statistics, Xidian University, Xi’an 710071,China
Keywords:
Drug-target interactions prediction, drug discovery, machine learning, computational methods, supervised learning, semisupervised
learning, unsupervised learning.
Abstract:
Background: Drug-Target interactions are vital for drug design and drug repositioning. However,
traditional lab experiments are both expensive and time-consuming. Various computational methods which applied
machine learning techniques performed efficiently and effectively in the field.
Results: The machine learning methods can be divided into three categories basically: Supervised methods,
Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying
machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used
in drug discovery. In addition, we compared the advantages and limitations of these methods in each category.
Conclusion: Every prediction model has both strengths and weaknesses and should be adopted in proper ways.
Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic
results due to the biases and the exploiting of regression models on DTIs prediction should be seriously
considered.
