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Journal ArticleDOI

Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research

01 Jun 2000-Journal of Pharmaceutical and Biomedical Analysis (Elsevier)-Vol. 22, Iss: 5, pp 717-727
TL;DR: Artificial neural networks are biologically inspired computer programs designed to simulate the way in which the human brain processes information and represent a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes.
About: This article is published in Journal of Pharmaceutical and Biomedical Analysis.The article was published on 2000-06-01. It has received 1144 citations till now. The article focuses on the topics: Time delay neural network & Learning rule.
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Journal ArticleDOI
01 Nov 2018-Heliyon
TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.

1,471 citations


Cites methods from "Basic concepts of artificial neural..."

  • ...The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [11, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194]....

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Posted Content
TL;DR: This paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.
Abstract: Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.

878 citations


Additional excerpts

  • ...Typical biological inspired neuron [5]...

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Journal ArticleDOI
TL;DR: An overview of this emerging field of molecular informatics, the basic concepts of prominent deep learning methods are presented, and motivation to explore these techniques for their usefulness in computer‐assisted drug discovery and design is offered.
Abstract: Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of “deep learning”. Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.

538 citations

Journal ArticleDOI
TL;DR: A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of Alzheimer's disease was performed by as mentioned in this paper, where a PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018.
Abstract: Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as-omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.

296 citations

Journal ArticleDOI
TL;DR: The tools of microfabrication technology, information science, and systems biology are being combined to design increasingly sophisticated drug delivery systems that promise to significantly improve medical care.
Abstract: Micro- and nano-electromechanical systems (MEMS and NEMS)-based drug delivery devices have become commercially-feasible due to converging technologies and regulatory accommodation. The FDA Office of Combination Products coordinates review of innovative medical therapies that join elements from multiple established categories: drugs, devices, and biologics. Combination products constructed using MEMS or NEMS technology offer revolutionary opportunities to address unmet medical needs related to dosing. These products have the potential to completely control drug release, meeting requirements for on-demand pulsatile or adjustable continuous administration for extended periods. MEMS or NEMS technologies, materials science, data management, and biological science have all significantly developed in recent years, providing a multidisciplinary foundation for developing integrated therapeutic systems. If small-scale biosensor and drug reservoir units are combined and implanted, a wireless integrated system can regulate drug release, receive sensor feedback, and transmit updates. For example, an "artificial pancreas" implementation of an integrated therapeutic system would improve diabetes management. The tools of microfabrication technology, information science, and systems biology are being combined to design increasingly sophisticated drug delivery systems that promise to significantly improve medical care.

294 citations

References
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Book
01 Jan 1996
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.
Abstract: From the Publisher: Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.

5,632 citations

Book
01 Jan 1992
TL;DR: Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.
Abstract: Jacek M. Zurada received his MS and Ph.D. degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. Since 1989 he has been a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky. He was Department Chair from 2004 to 2006. He has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits. INTRODUCTION TO ARTIFICIAL NEURAL SYSTEMS

2,883 citations

Journal ArticleDOI
TL;DR: In this article, a review of neural networks in chemistry is presented, focusing on the back-propagation algorithm and its applications in spectroscopy, protein structure, process control and chemical reactivity.

547 citations

Journal ArticleDOI
TL;DR: A nonlinear computational neural network model developed by using the genetic algorithm with a neural network fitness evaluator to estimate percent human intestinal absorption (%HIA) is an attractive alternative to experimental measurements.
Abstract: Prediction of human intestinal absorption (HIA) is a major goal in the development of oral drugs. The application of combinatorial chemistry methods to drug discovery has dramatically increased the demand for rapid and efficient models for estimating HIA and other biopharmaceutical properties. While experimental methods for measurement of intestinal absorption have been developed and are used widely, computational approaches provide an attractive alternative.

317 citations

Journal ArticleDOI
TL;DR: A new hybrid method (GNN) combining a genetic algorithm and an artificial neural network has been developed for quantitative structure-activity relationship (QSAR) studies, and it is essential to have one each for the steric, electrostatic, and hydrophobic attributes of a drug candidate to obtain a satisfactory QSAR for this data set.
Abstract: A new hybrid method (GNN) combining a genetic algorithm and an artificial neural network has been developed for quantitative structure-activity relationship (QSAR) studies A suitable set of molecular descriptors are selected by a genetic algorithm This set serves as input to a neural network, in which model-free mapping of multivariate data is performed Multiple predictors are generated that are superior to results obtained from previous studies of the Selwood data set, which is used to test the method The neural network technique provides a graphical description of the functional form of the descriptors that play an important role in determining drug activity This can serve as an aid in future design of drug analogues The effectiveness of GNN is tested by comparing its results with a benchmark obtained by exhaustive enumeration Different fitness strategies that tune the evolution of genetic models are examined, and QSARs with higher predictiveness are found From these results, a composite model is constructed by averaging predictions from several high-ranking models The predictions of the resulting QSAR should be more reliable than those derived from a single predictor because it makes greater use of information and also permits error estimation An analysis of the sets of descriptors selected by GNN shows that it is essential to have one each for the steric, electrostatic, and hydrophobic attributes of a drug candidate to obtain a satisfactory QSAR for this data set This type of result is expected to be of general utility in designing and understanding QSAR

281 citations