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Indian Journal of Biochemistry and Biophysics (IJBB) >
IJBB Vol.49 [2012] >
IJBB Vol.49(3) [June 2012] >
| Title: | Linear and non-linear quantitative structure-activity relationship models on indole substitution patterns as inhibitors of HIV-1 attachment |
| Authors: | Nirouei, Mahyar
Ghasemi, Ghasem
Abdolmaleki, Parviz
Tavakoli, Abdolreza
Shariati, Shahab |
| Keywords: | HIV
Indole glyoxamide derivatives
Quantitative structure-activity relationship
Genetic algorithm
Artificial neural network
Multiple linear regressions |
| Issue Date: | Jun-2012 |
| Publisher: | NISCAIR-CSIR, India |
| Abstract: | The
antiviral drugs that inhibit human immunodeficiency virus (HIV) entry to the
target cells are already in different phases of clinical trials. They prevent
viral entry and have a highly specific mechanism of action with a low toxicity
profile. Few QSAR studies have been performed on this group of inhibitors. This
study was performed to develop a quantitative structure–activity relationship
(QSAR) model of the biological activity of indole glyoxamide derivatives as
inhibitors of the interaction between HIV glycoprotein gp120 and host cell CD4
receptors. Forty different indole glyoxamide derivatives were selected as a
sample set and geometrically optimized using Gaussian 98W. Different
combinations of multiple linear regression (MLR), genetic algorithms (GA) and
artificial neural networks (ANN) were then utilized to construct the QSAR
models. These models were also utilized to select the most efficient subsets of
descriptors in a cross-validation procedure for non-linear log (1/EC50)
prediction. The results that were obtained using GA-ANN were compared with
MLR-MLR and MLR-ANN models. A high predictive ability was observed for the MLR,
MLR-ANN and GA-ANN models, with root mean sum square errors (RMSE) of 0.99,
0.91 and 0.67, respectively (N = 40). In summary, machine learning methods were
highly effective in designing QSAR models when compared to statistical method. |
| Page(s): | 202-210 |
| CC License: |  CC Attribution-Noncommercial-No Derivative Works 2.5 India |
| ISSN: | 0975-0959 (Online); 0301-1208 (Print) |
| Source: | IJBB Vol.49(3) [June 2012]
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