QSAR Modeling ANTI-HIV-1 Activities by Optimization of Correlation Weights of Local Graph Invariants

Abstract

Results of using descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase (RT) inhibitors are reported. Presence of different chemical elements in molecular structure of the inhibitors and the presence of Morgan extended connectivity values of zeroth-, first- and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. By Monte Carlo method optimization procedure, values of the CWs which produce as large values as possible of correlation coefficient between the numerical data on the anti-HIV-1 potencies and values of the descriptors on the training set have been computed. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of presence of chemical elements together with the presence of Morgan extended connectivity of first order is reasonable well model for the prediction of endpoints under consideration for compounds of the test set.

Keywords:

• QSAR Modeling
• Anti-HIV-1 activity
• Correlation weight of local graph invariants
• Flexible topological descriptors

Acknowledgements

Authors wish to express their gratefulness to scientific international fund of the Third Word Academy of Sciences (TWAS) for supporting this study.