ChemInform Abstract: Application of Neutral Networks in Structure—Activity Relationships

Koevesdi, I.; Dominguez‐Rodriguez, Maria Felisa; Őrfi, László; Náray‐Szabó, Gábor; Varró, András; Papp, Julius Gy.; Mátyus, Péter

doi:10.1002/chin.199929291

Cited by 2 publications

(8 citation statements)

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“…Recently, González-Diaz et al 34 and Vilar et al 35 used the ANN model to built QSAR model for cancer and HIV studies, respectively. However, the feed-forward backpropagation ANN has been the most popular in QSAR analyses 33,36,37 and is the focus of our study. Here, we used a multilayer perceptron (MLP) network for our study on ARIs.…”

Section: Qsarmentioning

confidence: 99%

Artificial neural networks‐based approach to design ARIs using QSAR for diabetes mellitus

Patra

Singh

2009

J Comput Chem

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In this article, in the first part, we propose an artificial neural network-based intelligent technique to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARIs) for diabetes mellitus using two molecular descriptors, i.e., the electronegativity and molar volume of functional groups present in the main ARI lead structure. We have shown that the multilayer perceptron-based model is capable of determining the QSAR quite satisfactorily, with high R-value. Usually, the design of potent ARIs requires the use of complex computer docking and quantum mechanical (QM) steps involving excessive time and human judgement. In the second part of this article, to reduce the design cycle of potent ARIs, we propose a novel ANN technique to eliminate the computer docking and QM steps, to predict the total score. The MLP-based QSAR models obtained in the first part are used to predict the potent ARIs, using the experimental data reported by Hu et al. (J Mol Graph Mod 2006, 24, 244). The proposed ANN-based model can predict the total score with an R-value of 0.88, which indicates that there exists a close match between the predicted and experimental total scores. Using the ANN model, we obtained 71 potent ARIs out of 6.25 million new ARI compounds created by substituting different functional groups at substituting sites of main lead structure of known ARI. Finally, using high bioactivity relationship and total score values, we determined four potential ARIs out of these 71 compounds. Interestingly, these four ARIs include the two potent ARIs reported by Hu et al. (J Mol Graph Mod 2006, 24, 244) who obtained these through the complex computer docking and QM steps. This fact indicates the effectiveness of our proposed ANN-based technique. We suggest these four compounds to be the most promising candidates for ARIs to prevent the diabetic complications and further recommend for wet bench experiments to find their potential against AR in vitro and in vivo.

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Section: Qsarmentioning

confidence: 99%

Artificial neural networks‐based approach to design ARIs using QSAR for diabetes mellitus

Patra

Singh

2009

J Comput Chem

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“…The rate of decrease in absorbance at 517 nm was accessed spectrophotometrically, which represents the capacity DPPH-RS and the activity is stated as IC 25 (in lM). IC 25 represents the concentration of the test compound needed to scavenge 25% of the DPPH radicals. For the purpose of this study, the IC 25 has been converted into log 1/IC 25 .…”

Section: Datasetsmentioning

confidence: 99%

“…IC 25 represents the concentration of the test compound needed to scavenge 25% of the DPPH radicals. For the purpose of this study, the IC 25 has been converted into log 1/IC 25 . Table 2 provides DPPH-RS activities of 15 flavone and flavonol compounds (shown as Target) with functional groups substitution at locations shown in Figure 2.…”

Section: Datasetsmentioning

confidence: 99%

“…22 Recently, González-Diaz et al 23 and Vilar et al 24 used the ANN model to build QSAR model for cancer and HIV studies, respectively. The feed-forward backpropagation ANN has been the most popular in QSAR analyses, 22,25,26 and it is the focus of the current study. Recently, Patra and Singh 27 proposed ANN-based QSAR model for prediction of potent ARI compounds for DM.…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen that LM4 has the overall highest R value and lowest RMSE compared with the other 19 combinations and therefore, LM4 architecture was used for stage 2. The predicted IC 25 values are listed in the last column of Table 2. Figure 10 …”

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confidence: 99%

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Artificial neural network‐based drug design for diabetes mellitus using flavonoids

Patra

Chua

2010

J Comput Chem

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Diabetes mellitus is a chronic metabolic disease involving the failure to regulate glucose blood levels in the body and has been linked with numerous detrimental complications. Studies have shown that these complications can be linked to the activities of aldose reductase (AR), an enzyme of the polyol pathway. Flavonoids have been identified as good AR inhibitors (ARIs) and are also strong antioxidants with radical scavenging (RS) activity. As such, flavonoids show potential to become a better class of ARIs because they are able to concurrently address the oxidative stress issue. In this article, we carried out quantitative structure-activity relationship analysis of flavones and flavonols (members of flavonoid family) using artificial neural networks. Three computer experiments were conducted to study the influence of hydrogen (H), hydroxyl (-OH), and methoxyl (-CH(3)) functional groups on eight substitution sites of the lead flavone molecule and to predict potential ARIs. Of 6561 possible flavones and flavonols, in experiment 1, we predicted 69 potent ARIs, and in experiment 2, we predicted 346 compounds with strong RS activity. In experiment 3, we combined these results to find overlapping compounds with both strong AR inhibition and RS activity and we are able to predict 10 potent compounds with strong AR inhibition (IC(50) < 0.3 μM) and RS activity (IC(25) < 1.0 μM). These 10 compounds show promise of being good therapeutic agents in the prevention of diabetic complications and is suggested to undergo further wet bench experimentation to prove their potency.

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ChemInform Abstract: Application of Neutral Networks in Structure—Activity Relationships

Cited by 2 publications

References 1 publication

Artificial neural networks‐based approach to design ARIs using QSAR for diabetes mellitus

Artificial neural networks‐based approach to design ARIs using QSAR for diabetes mellitus

Artificial neural network‐based drug design for diabetes mellitus using flavonoids

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