Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1

Abstract

Researchers are on the constant lookout for new antiviral agents for the treatment of AIDS. In the present work, ligand based modeling studies are performed on analogues of substituted phenyl-thio-thymines, which act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) and novel leads are extracted. Using alignment-dependent descriptors, based on group center overlap (SALL, HDALL, HAALL and RALL), an alignment-independent descriptor (S log P), a topological descriptor (Balaban index (J)) and a 3D descriptor dipole moment (μ) and shape based descriptors (Kappa 2 index ((2)κ)), a correlation is derived with inhibitory activity. Linear and non-linear techniques have been used to achieve the goal. Support Vector Machine (SVM, R = 0.929, R(2) = 0.863) and Back Propagation Neural Network (BPNN, R = 0.928, R(2) = 0.861) methods yielded near similar results and outperformed Multiple Linear Regression (MLR, R = 0.915, R(2) = 0.837). The predictive ability of the models are cross-validated using a test dataset (SVM: R = 0.846, R(2) = 0.716, BPNN: R = 0.841, R(2) = 0.707 and MLR: R = 0.833, R(2) = 0.694). It is concluded that the hydrophobicity (S log P) and the polarity (μ) of a ligand and the presence of hydrogen donor (HDALL) moieties are the deciding factors in improving antiviral activity and pharmaco-therapeutic properties. Based on the above findings, a virtual dataset is created to extract probable leads with reasonable antiviral activity as well as better pharmacophoric properties.

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

“…25,26 Support vector machine and back-propagation neural networks were found to outperform multiple linear regression in training and testing in the modeling of analogs of substituted phenyl-thio-thymines as NNRTI. 27 Support vector machine was also used to model RT inhibitors and to improve the speed of virtual screening and the enrichment factor. 28 Machine learning has been used in a few limited examples for other targets such as integrase.…”

“…We have previously used Bayesian and other machine learning methods for tuberculosis, − Chagas disease, and Ebola virus drug discovery to identify new molecules for testing. Public databases such as ChEMBL have been used previously to develop support vector machine models for several viruses, including HIV. , Support vector machine and back-propagation neural networks were found to outperform multiple linear regression in training and testing in the modeling of analogs of substituted phenyl-thio-thymines as NNRTI . Support vector machine was also used to model RT inhibitors and to improve the speed of virtual screening and the enrichment factor .…”

Machine learning approaches to study HIV/AIDS infection: A Review

“…Prediction of antibody of HIV epitope networks using neutralization titers and a novel computational methods or a simple machine learning methods has been done in USA (Evans et al 2014Hepler et al 2014and Choi et al 2015 Prediction of HIV-1 RT associated RNase H inhibition (Poongavanam 2013) shown good enrichment (80-90%) by receptor-based fl exible docking experiments compared to ligand-based approaches such as FLAP (74%), shape similarity (75%) and random forest (72%) in Denmark. Ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors of HIV-1 (Pancholi et al 2014) has been done India respectively. Prediction of bioactivities of HIV-1 integrase ST inhibitors (Xuan et al 2013) and classifi cation of active and weakly active ST inhibitors of HIV-1 integrase has (Yan et al 2012) been done using machine learning approaches in China and USA respectively.…”

Halolactones are potent HIV-1 non-nucleoside reverse transcriptase inhibitors

“…In the life cycle of HIV-1, reverse transcriptase (RT), as one of the three important enzymes, contributes to the conversion of single-stranded viral RNA into double-stranded proviral DNA, a prerequisite for integration into host DNA. 1,2 RT inhibitors work by blocking the action of HIV's enzyme reverse transcriptase, stopping the virus from replicating in cells. 3 Its inhibition is considered to be one of the most practicable approaches for preventing the spread of infection.…”