Journal ArticleDOI
Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research
TLDR
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.read more
Citations
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Journal ArticleDOI
State-of-the-art in artificial neural network applications: A survey
Oludare Isaac Abiodun,Oludare Isaac Abiodun,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Nachaat AbdElatif Mohamed,Humaira Arshad +6 more
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.
Posted Content
Activation Functions: Comparison of trends in Practice and Research for Deep Learning
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.
Journal ArticleDOI
Deep Learning in Drug Discovery.
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.
Journal ArticleDOI
Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data
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.
Journal ArticleDOI
Application of Micro- and Nano-Electromechanical Devices to Drug Delivery
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.
References
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Book
Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
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.
Book
Introduction to artificial neural systems
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.
Journal ArticleDOI
Neural networks: A new method for solving chemical problems or just a passing phase?
Jure Zupan,Johann Gasteiger +1 more
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.
Journal ArticleDOI
Prediction of human intestinal absorption of drug compounds from molecular structure.
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.
Journal ArticleDOI
Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks.
Sung-Sau So,Martin Karplus +1 more
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.
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