Title:Identification of Anticancer and Anti-inflammatory Drugs from Drugtarget
Interaction Descriptors by Machine Learning
Volume: 19
Issue: 9
Author(s): Songtao Huang and Yanrui Ding*
Affiliation:
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, P.R. China
- Key Laboratory of Industrial Biotechnology,
Jiangnan University, Wuxi, Jiangsu 214122, P.R. China
Keywords:
Drug-target interaction, molecular docking, descriptors, SVM, LightGBM, MLP.
Abstract:
Background: Drug repositioning is an important subject in drug-disease research. In the past,
most studies simply used drug descriptors as the feature vector to classify drugs or targets or used qualitative
data about drug-target or drug-disease to predict drug-target interactions. These data provide limited
information for drug repositioning.
Objective: Considering both drugs and targets and constructing quantitative drug-target interaction descriptors
as a method of drug characteristics are of great significance to the study of drug repositioning.
Methods: Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between
drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors
were calculated to describe the drug-target interactions, and 22 important descriptors were
screened for drug classification by SVM, LightGBM, and MLP.
Results: The accuracy of SVM, LightGBM, and MLP reached 93.29%, 92.68%, and 94.51%, their Matthews
correlation coefficients reached 0.852, 0.840, and 0.882, and their areas under the ROC curve
reached 0.977, 0.969, and 0.968, respectively.
Conclusion: Using drug-target interaction descriptors to build machine learning models can obtain better
results for drug classification. Number of atom pairs, force field, hydrophobic interactions, and bSASA
are the key features for classifying anticancer and anti-inflammatory drugs.
