Abstract
Quantitative Structure Activity Relationships (QSAR or SAR) have helped scientists to establish mathematical relationships between molecular structures and their biological activities. In the present article, SAR studies have been carried out on 89 tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives using different classifiers, such as support vector machines, artificial neural networks, random forests, and decision trees. The goal is to propose classification models that will be able to classify TIBO compounds into two groups: high and low inhibitors of HIV-1 reverse transcriptase. Each molecular structure was encoded by 10 descriptors. To check the validity of the established models, all of them were subjected to various validation tests: internal validation, Y-randomization, and external validation. The established classification models have been successful. The correct classification rates reached 100% and 90% in the learning and test sets, respectively. Finally, molecular docking analysis was carried out to understand the interactions between reverse transcriptase enzyme and the TIBO compounds studied. Hydrophobic and hydrogen bond interactions led to the identification of active binding sites. The established models could help scientists to predict the inhibition activity of untested compounds or of novel molecules prior to their synthesis. Therefore, they could reduce the trial and error process in the design of human immunodeficiency virus (HIV) inhibitors.
Highlights
Human immunodeficiency virus (HIV) is a member of a family of viruses called retroviruses and belongs to a subgroup called lentiviruses
Reverse transcriptase (RT) has become a subject of considerable pharmaceutical research and a major target of anti-acquired immunodeficiency syndrome (AIDS) drug design [1]
A large number of inhibitors have been designed [2], synthesized, and assayed, and some HIV-1 RT inhibitors are utilized in the treatment of AIDS
Summary
Human immunodeficiency virus (HIV) is a member of a family of viruses called retroviruses and belongs to a subgroup called lentiviruses. Enzymes responsible for HIV-1 replication have been identified as therapeutic targets. Reverse transcriptase (RT) is one of the main targets for antiretroviral drug development due to its essential role in the HIV replication. HIV uses its RT to convert its RNA genome into DNA Inhibition of this activity impedes HIV’s ability to replicate and to infect additional cells. Pharmaceuticals 2018, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/pharmaceuticals Since it is a classification study (qualitative), the original dependent variable (log (1/IC50)) was divided into two classes:. - Class H includes compounds with high activities (i.e., log (1/IC50) ≥ 5.79). - Class L contains compounds with low activities (i.e., log (1/IC50) < 5.79). The data set (89 compounds) was divided into a training set (69 compounds) and a test set (20 compounds), where the former set is used to develop the classifier and the latter to evaluate its performance.
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 callDisclaimer: 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.
