Mapping the dose-effect relationship of orbifan from sparse data with an artificial neural network
A neural network (NN) pharmacodynamic model was developed that correlates the inhibition of ex vivo platelet aggregation by orbofiban, an oral glycoprotein IIb/IIIa antagonist, with the administered dose and patient characteristics. Data were obtained from a Phase-II dose-finding study of orbofiban in patients presenting with acute coronary syndromes. A back-propagation NN was designed to predict drug effect measured at predose and 4 and 6 h on treatment days 1, 28, and 84 (nine responses/patient). The training set consisted of patients for whom complete response profiles were reported (n = 67), and remaining patients were included in the validation data set (n = 47). The concentration-effect relationship was described additionally using a population direct-effect inhibitory sigmoidal model, and a comparison of the predictive performances of both models was performed. The final NN reasonably described orbofiban pharmacodynamics from sparse data sets without specifying a structural model or drug concentrations. Despite considerable inter-patient variability in response-time profiles, the population model revealed a strong correlation between drug concentration and effect and exhibited greater precision than the NN model. Although the population model showed greater precision, these results suggest that NNs may be useful for individualizing pharmacotherapy when drug concentrations are relatively unpredictable or unavailable.
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Available from: Jadson Castro
- ". ANNs techniques have been also used in the fields of robotics, pattern identification, psychology , physics, computer science, biology and many others [37- 40]. In addition, ANNs have been applied to the modeling of several systems in a wide range of applications such as animal science , cancer imaging extraction and classification  , pharmacodynamic and pharmacokinetic modeling   and mapping of dose–effect relationships on pharmacological response , to predict secondary structures  and transmembrane segments , simulation of C13 nuclear magnetic resonance spectra , prediction of drug resistance of HIV-1 protease ligands , prediction of toxicity of chemicals to aquatic species , and as well as predicting physicochemical properties from the perspective of pharmaceutical research . "
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ABSTRACT: 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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Available from: ci.uofl.edu
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ABSTRACT: The wire bonding process is the key process in an IC chip-package. It is an urgent problem for IC chip-package industry to
improve the wire bonding process capability. In this study, an integrated system is proposed to identify and control parameters
in the wire bonding process in order to achieve high level performance and quality. First, an experimental design with Taguchi
method is applied to identify the critical parameters in the wire bonding process. Then, an ANN is used to establish the nonlinear
multivariate relationships between wire boning parameters and responses. Finally, a GA is adopted to find the most desired
parameter settings by using the output of ANN as the fitness measure. Another popular method, response surface method, for
parameter design problems is conducted for comparison purpose. Results of this comparison demonstrate the effectiveness of
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International Journal of Advanced Manufacturing Technology 08/2006; 30(3):247-253. DOI:10.1007/s00170-005-0083-0 · 1.46 Impact Factor
Available from: Peter A Crooks
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ABSTRACT: Back-propagation artificial neural networks (ANNs) were trained on a dataset of 104 VMAT2 ligands with experimentally measured log(1/K(i)) values. A set of related descriptors, including topological, geometrical, GETAWAY, aromaticity, and WHIM descriptors, was selected to build nonlinear quantitative structure-activity relationships. A partial least squares (PLS) regression model was also developed for comparison. The nonlinearity of the relationship between molecular descriptors and VMAT2 ligand activity was demonstrated. The obtained neural network model outperformed the PLS model in both the fitting and predictive ability. ANN analysis indicated that the computed activities were in excellent agreement with the experimentally observed values (r(2)=0.91, rmsd=0.225; predictive q(2)=0.82, loormsd=0.316). The generated models were further tested by use of an external prediction set of 15 molecules. The nonlinear ANN model has r(2)=0.93 and root-mean-square errors of 0.282 compared with the experimentally measured activity of the test set. The stability test of the model with regard to data division was found to be positive, indicating that the generated model is predictive. The modeling study also reflected the important role of atomic distribution in the molecules, size, and steric structure of the molecules when they interact with the target, VMAT2. The developed models are expected to be useful in the rational design of new chemical entities as ligands of VMAT2 and for directing synthesis of new molecules in the future.
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