A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors

Hu, Lihong; Chen, Guanhua; Chau, Raymond Ming-Wah

doi:10.1016/j.jmgm.2005.09.002

Cited by 30 publications

(24 citation statements)

References 35 publications

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“…From the list generated in experiment 1 stage 2, of the 6561 flavone and flavonol compounds, we have found that the predicted ARI activity range is quite large, i.e., it ranges from 19 to 236,500 nM. Hu et al 40 reported that potent spirohydantoins ARI compounds have a cutoff concentration between 30 and 40 nM. Considering this fact, in this study, we have set a lower cutoff concentration of \30 nM to show that flavonoids would make better candidates for Figure 7.…”

Section: Experiments 1: Stage 3-best Predicted Ari Compoundsmentioning

confidence: 94%

“…These two descriptors were calculated by Hu et al 40 using ab initio Hartree-Fock method with 6-311G (d,p) basic set and zero point energy correlation. The calculations were carried out using Gaussian 98 program.…”

Section: Quantitative Structure-activity Relationship (Qsar)mentioning

confidence: 99%

“…For ARI, the concentration is less than 30 nM. This cutoff was chosen to show that flavonoids are a better class of ARIs compared to spirohydantoins which was proposed by Hu et al 40 with a cutoff concentration between 30 and 40 nM. 2.…”

Section: Experiments 3: Combination Of Ari and Dpph-rs Activitiesmentioning

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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Section: Experiments 1: Stage 3-best Predicted Ari Compoundsmentioning

confidence: 94%

Section: Quantitative Structure-activity Relationship (Qsar)mentioning

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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“…Another important approach to design new potential drugs is virtual screening (VS), which can maximize the effectiveness of rational drug development employing computational assays to classify or filter a compound database as potent drug candidates [96][97][98][99][100]. Besides, various ANN methodologies have been largely applied to control the process of the pharmaceutical production [101][102][103][104].…”

Section: Medicinal Chemistry and Pharmaceutical Researchmentioning

confidence: 99%

Applications of Artificial Neural Networks in Chemical Problems

Matarollo¹,

Honório²,

Silva³

2013

Artificial Neural Networks - Architectures and Applications

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“…An ANN with suitable set of topological descriptors and training algorithms was used to determine the minimum inhibitory concentration of quinolones. In another example, Hu et al [2] combined ANNs, quantum chemistry and molecular docking methods to design novel Aldose Reductase Inhibitors (ARIs). Physicochemical parameters including electronegativity and molar volume of known inhibitors were calculated using quantum chemistry methods.…”

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confidence: 99%

Applications of Artificial Neural Network Modeling in Drug Discovery

Cheng

Sutariya²

2012

Clin Exp Pharmacol

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Artificial Neural Networks (ANNs) modeling is a group of computer algorithms for modeling and pattern recognition, functioning similarly to the neurons of the brain. The brain learns from its experience. In the brain, a biological neuron receives inputs from many external resources, combines them, performs a non-linear operation, and then makes a decision based on the final results. The ANNs are a type of mathematical model that simulates the biological nervous system and draws on analogues of adaptive biological neurons. A major advantage of ANNs compared to statistical modeling is that they do not require rigidly structured experimental designs and can map functions using historical or incomplete data. ANNs are good recognizers of patterns and robust classifiers, with the ability to generate when making decisions based on imprecise input data.ANNs are known to be a powerful tool to simulate various nonlinear systems and have been applied to numerous problems of considerable complexity in many field including pharmaceutical research, engineering, psychology and medicinal chemistry.The potential applications of ANN methodology in the pharmaceutical sciences are broad. In this paper, we will review some applications of ANNs in drug discovery. Quantitative Structure-Activity Relationship (QSAR)QSAR correlates physicochemical parameters of compounds with chemical or biological activities. These parameters include topological parameters, molecular weight, molar volume, electronegativity, logP, hydrogen acceptor, hydrogen donor and molar refractivity. ANNs have been shown to be an effective tool to establish this type of relationship and predict the activities of new compounds. Jaen-Oltra et al.[1] developed a new topological method to predict antimicrobial properties of quinolones derivatives on the basis of their chemical structures. An ANN with suitable set of topological descriptors and training algorithms was used to determine the minimum inhibitory concentration of quinolones. In another example, Hu et al. [2] combined ANNs, quantum chemistry and molecular docking methods to design novel Aldose Reductase Inhibitors (ARIs). Physicochemical parameters including electronegativity and molar volume of known inhibitors were calculated using quantum chemistry methods. Using these parameters as input nodes, an ANN based QSAR model was constructed and the biological activities of new compounds were predicted. The further molecular docking analysis showed all the predicted potent compounds by ANNs binded well to the active site of the aldose reductase. Virtual Screening (VS)Virtual screening is a novel approach to speed the drug discovery process. It applies computational methods to quickly search (or "screen") compounds with known chemical structures to identify the compounds with high biological activities in a compound database. ANNs-based QSAR models are widely chosen as the prediction methods in the virtual screening. Recently, Myint et al. [3] have developed a 2D fingerprint-based artificial neural network QSAR (FA...

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A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors

Cited by 30 publications

References 35 publications

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

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

Applications of Artificial Neural Networks in Chemical Problems

Applications of Artificial Neural Network Modeling in Drug Discovery

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