QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence

Author(s): Elham Doosti, Mohsen Shahlaei

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 18 , Issue 8 , 2015

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Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An exact study of predictability of PCR and PC-ANN showed that the later model has much higher ability to calculate the biological activity of the investigated molecules.

Also, experimental and estimated biological activities of compounds used in model development step have indicated a good correlation.

Obtained results show that a non-linear model explaining the relationship between the pIC50s and the calculated principal components (that extract from structural descriptors of the studied molecules) is superior than linear model.

Some typical figures of merit for QSAR studies explaining the accuracy and predictability of the suggested models were calculated.

Therefore, to design novel inhibitors of p38 map kinase with high potency and low undesired effects the developed QSAR models were used to estimate biological pIC50 of the studied compounds.

Keywords: ANN, p38 MAP kinase, PCA, QSAR.

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Article Details

Year: 2015
Page: [767 - 783]
Pages: 17
DOI: 10.2174/1386207318666150803144322
Price: $65

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