Author/Authors :
Zhou, Junlin Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Hao, Juan Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Peng, Lianxin Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Duan, Huaichuan Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Luo, Qing Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Yan, Hailian Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Wan, Hua South China Agricultural University - Guangzhou, China , Hu, Yichen Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Liang, Li Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Xie, Zhenjian Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Liu, Wei Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Zhao, Gang Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China , Hu, Jianping Sichuan Education Department - Sichuan Industrial Institute of Antibiotics - Chengdu University - Chengdu, China
Abstract :
A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the
host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors
were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The
database was divided into the training set and test set by random sampling. By exploring the correlation between molecular
descriptors and inhibitory activity, it is found that the classification and specific activity data of inhibitors can be more
accurately predicted by the combination of molecular descriptors and molecular fingerprints. The calculation of molecular
fingerprint descriptor provides the additional substructure information to improve the prediction ability. Based on the training
set, two machine learning methods, the recursive partition (RP) and naive Bayes (NB) models, were used to build the classifiers
of HIV-1 IN inhibitors. Through the test set verification, the RP technique accurately predicted 82.5% inhibitors and 86.3%
noninhibitors. The NB model predicted 88.3% inhibitors and 87.2% noninhibitors with correlation coefficient of 85.2%. The
results show that the prediction performance of NB model is slightly better than that of RP, and the key molecular segments are
also obtained. Additionally, CoMFA and CoMSIA models with good activity prediction ability both were constructed by
exploring the structure-activity relationship, which is helpful for the design and optimization of HIV-1 IN inhibitors.
