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.
Highlights
Acquired immune deficiency syndrome (AIDS) is a systemic immune dysfunction syndrome caused by the infection of human immunodeficiency virus (HIV) infection, inducing the destruction of CD4+ T lymphocytes [1,2,3]
The relationship between molecular descriptors and their inhibitory activities was systematically studied through the recursive partition (RP) and naive Bayes (NB) methods
The prediction performance of the two classification models based on the combination of molecular descriptor with molecular fingerprint than that based on the individual molecular descriptor
Summary
Acquired immune deficiency syndrome (AIDS) is a systemic immune dysfunction syndrome caused by the infection of human immunodeficiency virus (HIV) infection, inducing the destruction of CD4+ T lymphocytes [1,2,3]. HIV can be divided into two subtypes: HIV-1 (i.e., the main pathogen of AIDS) and HIV-2. HIV-1 is characterized by strong infection, rapid mutation, and high mortality and can be transmitted through blood, mother-infant, sexual intercourse, etc. Since the first case of HIV-1 infection in 1981, the number of AIDS patients has exploded worldwide [9]. Active antiretroviral therapy (HAART) is the main strategy in the clinical treatment of AIDS—the combination of drugs inhibiting both reverse transcriptase (RT) and the protease (PR), which can reduce the damage of virus to immune system [11]. The high variability of HIV-1 results in poor efficacy of HAART treatment, leading to the emergence of drugresistant virus strains. It is urgent to identify new targets and develop novel structural inhibitors [12,13,14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational and mathematical methods in medicine
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
