Mapping the Dose–Effect Relationship of Orbofiban from Sparse Data with an Artificial Neural Network

Mager, Donald E.; Shirey, J. D.; Cox, Dermot; Fitzgerald, Desmond J.; Abernethy, Darrell R.

doi:10.1002/jps.20384

Cited by 11 publications

(9 citation statements)

References 20 publications

(24 reference statements)

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“…Table 13 shows that compounds 132–135 , which constitute 4 different stereoisomers of the same structure, have different binding affinities for VMAT2. The experiment measurements for compounds 132 and 135 were consistent with theoretical predictions [106]. …”

Section: Computational Neural Network Analysis Of the Affinity Of supporting

confidence: 82%

“…The QSAR study revealed that while the linear partial least squares model built from the same dataset is predictive, the fully interconnected three-layer neural network model trained with the back-propagation procedure was superior learning the correct association between a set of relevant descriptors of compounds and the log(1/K i ) for VMAT2 [106]. The optimal ANN architecture was determined as 11:3:1, i.e.…”

Section: Computational Neural Network Analysis Of the Affinity Of mentioning

confidence: 99%

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Design, Synthesis and Interaction at the Vesicular Monoamine Transporter-2 of Lobeline Analogs: Potential Pharmacotherapies for the Treatment of Psychostimulant Abuse

Crooks¹,

Zheng²,

Vartak³

et al. 2011

CTMC

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The vesicular monoamine transporter-2 (VMAT2) is considered as a new target for the development of novel therapeutics to treat psychostimulant abuse. Current information on the structure, function and role of VMAT2 in psychostimulant abuse are presented. Lobeline, the major alkaloidal constituent of Lobelia inflata, interacts with nicotinic receptors and with VMAT2. Numerous studies have shown that lobeline inhibits both the neurochemical and behavioral effects of amphetamine in rodents, and behavioral studies demonstrate that lobeline has potential as a pharmacotherapy for psychostimulant abuse. Systematic structural modification of the lobeline molecule is described with the aim of improving selectivity and affinity for VMAT2 over neuronal nicotinic acetylcholine receptors and other neurotransmitter transporters. This has led to the discovery of more potent and selective ligands for VMAT2. In addition, a computational neural network analysis of the affinity of these lobeline analogs for VMAT2 has been carried out, which provides computational models that have predictive value in the rational design of VMAT2 ligands and is also useful in identifying drug candidates from virtual libraries for subsequent synthesis and evaluation.

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Section: Computational Neural Network Analysis Of the Affinity Of supporting

confidence: 82%

Section: Computational Neural Network Analysis Of the Affinity Of mentioning

confidence: 99%

Design, Synthesis and Interaction at the Vesicular Monoamine Transporter-2 of Lobeline Analogs: Potential Pharmacotherapies for the Treatment of Psychostimulant Abuse

Crooks¹,

Zheng²,

Vartak³

et al. 2011

CTMC

View full text Add to dashboard Cite

show abstract

“…ANNs techniques have been also used in the fields of robotics, pattern identification, psychology, physics, computer science, biology and many others [37][38][39][40]. In addition, ANNs have been applied to the modeling of several systems in a wide range of applications such as animal science [41], cancer imaging extraction and classification [42,43], pharmacodynamic and pharmacokinetic modeling [44,45] and mapping of dose-effect relationships on pharmacological response [46], to predict secondary structures [47] and transmembrane segments [48], simulation of C13 nuclear magnetic resonance spectra [49], prediction of drug resistance of HIV-1 protease ligands [50], prediction of toxicity of chemicals to aquatic species [51], and as well as predicting physicochemical properties from the perspective of pharmaceutical research [52].…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Machine Learning Techniques and Drug Design

Gertrudes¹,

Maltarollo²,

Silva

et al. 2012

CMC

164

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The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.

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“…It has been shown that BP NN could approximate the PK and PD profiles generated from simulations of several structural PK/PD models (Gobburu and Chen, 1996). Furthermore, BP NN has been successfully applied in the bioequivalence study (Opara et al, 1999), population pharmacokinetics (Chow et al, 1997), clinical pharmacology (Brier and Aronoff, 1996;Urquidi-Macdonald et al, 2004;Mager et al, 2005) and PK/PD modeling (Veng-Pedersen and Modi, 1993;Haidar et al, 2002).…”

Section: Introductionmentioning

confidence: 97%

Using neural networks to determine the contribution of danshensu to its multiple cardiovascular activities in acute myocardial infarction rats

Chen¹,

Cao²,

Yuan³

et al. 2011

Journal of Ethnopharmacology

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Mapping the Dose–Effect Relationship of Orbofiban from Sparse Data with an Artificial Neural Network

Cited by 11 publications

References 20 publications

Design, Synthesis and Interaction at the Vesicular Monoamine Transporter-2 of Lobeline Analogs: Potential Pharmacotherapies for the Treatment of Psychostimulant Abuse

Design, Synthesis and Interaction at the Vesicular Monoamine Transporter-2 of Lobeline Analogs: Potential Pharmacotherapies for the Treatment of Psychostimulant Abuse

Machine Learning Techniques and Drug Design

Using neural networks to determine the contribution of danshensu to its multiple cardiovascular activities in acute myocardial infarction rats

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