TY - JOUR
T1 - Consensus features of CP-MLR and GA in modeling HIV-1 RT inhibitory activity of 4-benzyl/benzoylpyridin-2-one analogues
AU - Deshpande, Shreekant
AU - Singh, Rinki
AU - Goodarzi, Mohammad
AU - Katti, Seturam B.
AU - Prabhakar, Yenamandra S.
N1 - Funding Information:
One of the authors (S.D.) thanks CSIR, New Delhi, India, for the financial support in the form of Senior Research Fellowship. CDRI Communication No.7902.
PY - 2011/10
Y1 - 2011/10
N2 - The HIV-1 reverse transcriptase (RT) inhibitory activity of benzyl/benzoylpyridinones is modeled with molecular features identified in combinatorial protocol in multiple linear regression (CP-MLR) and genetic algorithm (GA). Among the features, nDB and LogP are found to be the most influential descriptors to modulate the activity. Although the coefficient of nDB suggested in favor of benzylpyridinones skeleton, the coefficient of LogP suggested the favorability of hydrophilic nature in compounds for better activity. The partial least squares analysis of the descriptors common to CP-MLR and GA has displayed their predictivity over the total descriptors identified in both the approaches. The back-propagation artificial neural networks model from the five most significant common descriptors (nDB, T(O.O), MATS8e, LogP, and BELp4) has explained 93.2% variance in the HIV-1 RT activity of the training set compounds and showed a test set r 2 of 0.89. The results suggest that the descriptors have the ability to identify the patterns in the compounds to predict potential analogues.
AB - The HIV-1 reverse transcriptase (RT) inhibitory activity of benzyl/benzoylpyridinones is modeled with molecular features identified in combinatorial protocol in multiple linear regression (CP-MLR) and genetic algorithm (GA). Among the features, nDB and LogP are found to be the most influential descriptors to modulate the activity. Although the coefficient of nDB suggested in favor of benzylpyridinones skeleton, the coefficient of LogP suggested the favorability of hydrophilic nature in compounds for better activity. The partial least squares analysis of the descriptors common to CP-MLR and GA has displayed their predictivity over the total descriptors identified in both the approaches. The back-propagation artificial neural networks model from the five most significant common descriptors (nDB, T(O.O), MATS8e, LogP, and BELp4) has explained 93.2% variance in the HIV-1 RT activity of the training set compounds and showed a test set r 2 of 0.89. The results suggest that the descriptors have the ability to identify the patterns in the compounds to predict potential analogues.
KW - ANN
KW - Benzyl/benzoylpyridinones
KW - CPMLR
KW - GA
KW - HIV-1 RT inhibitors
KW - QSAR
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U2 - 10.3109/14756366.2010.548328
DO - 10.3109/14756366.2010.548328
M3 - Article
C2 - 21284408
AN - SCOPUS:80052854752
SN - 1475-6366
VL - 26
SP - 696
EP - 705
JO - Journal of Enzyme Inhibition and Medicinal Chemistry
JF - Journal of Enzyme Inhibition and Medicinal Chemistry
IS - 5
ER -