QSAR STUDY FOR THE PREDICTION OF IC50 FOR 5-N-ACETYL-BETA-D-NEURAMINIC ACID STRUCTURALLY SIMILAR COMPOUNDS USING NEURAL NET

Authors

  • Ponmary Pushpa Latha D Assistant Professor (S.G.) Department of Computer Applications karunya university coimbatore
  • Joseph Pushpa Raj D

Abstract

Quantitative structure activity relationship (QSAR) study has been developed for structurally similar to 5-N-acetyl-Beta-D-Neuraminic acid as
inhibitors for Clostridium tetani causing targets using neural network. QSAR models for biological activity of half-maximal inhibitory concentration
50 (IC50) were created with 110 training compounds, 50 test compounds, and 16 different descriptors. The predictive capability of the QSAR models
was evaluated by r2, q2
LMO (TestSet), q2
LOO(TestSet), q2
BOOT(TestSet). The comparison of various external validation reveals identical q2
LMO(TestSet), q2
LOO(TestSet) and
q2
BOOT(TestSet) for IC50 (0.9) which demonstrates the high robustness and real predictive power of IC50 model.

Keywords: Quantitative structure activity relationship, Neural network, Inhibitory concentration 50, Leave-many-out, Leave-one-out, BOOT

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Published

2014-09-01

How to Cite

Latha D, P. P., and J. P. Raj D. “QSAR STUDY FOR THE PREDICTION OF IC50 FOR 5-N-ACETYL-BETA-D-NEURAMINIC ACID STRUCTURALLY SIMILAR COMPOUNDS USING NEURAL NET”. Asian Journal of Pharmaceutical and Clinical Research, vol. 7, no. 4, Sept. 2014, pp. 173-6, https://innovareacademics.in/journals/index.php/ajpcr/article/view/1552.

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