Applications of Artificial Neural Network in AIDS Research and Therapy

Abstract

In recent years considerable effort has been devoted to applying pattern recognition techniques to the complex task of data analysis in drug research. Artificial neural networks (ANN) methodology is a modeling method with great ability to adapt to a new situation, or control an unknown system, using data acquired in previous experiments. In this paper, a brief history of ANN and the basic concepts behind the computing, the mathematical and algorithmic formulation of each of the techniques, and their developmental background is presented. Based on the abilities of ANNs in pattern recognition and estimation of system outputs from the known inputs, the neural network can be considered as a tool for molecular data analysis and interpretation. Analysis by neural networks improves the classification accuracy, data quantification and reduces the number of analogues necessary for correct classification of biologically active compounds. Conformational analysis and quantifying the components in mixtures using NMR spectra, aqueous solubility prediction and structure-activity correlation are among the reported applications of ANN as a new modeling method. Ranging from drug design and discovery to structure and dosage form design, the potential pharmaceutical applications of the ANN methodology are significant. In the areas of clinical monitoring, utilization of molecular simulation and design of bioactive structures, ANN would make the study of the status of the health and disease possible and brings their predicted chemotherapeutic response closer to reality.

Neural Network Modeling in Dithiothreitol Reduction and Ion Treatment of Recombinant Human Insulin Obtained from the Circular Dichroism (CD) Spectral Information

“…In conclusion, there are various applications in the fields of ANN, including clinical data evaluation [49,50] and drug development and molecular studies [30]. This study came up to show the importance of CD and power of ANN in modeling data from spectropolarimetric analysis of peptides in settings including the pharmaceutical industry.…”

“…Each processing element has inputs, transfer functions and output. Processing elements are connected with coefficients and are arranged in layers, i.e., input layer, output layer and hidden layers in between [30]. Application of ANN in pharmaceutical research is a new field with novel potentials to be discovered.…”

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…ANNs have been applied in the diagnosis based on clinical chemical data of many diseases-cancers [139][140][141]; early diagnosis of lupus erythmatosus [142]; diagnosis of acute myocardial infarction [143,144]; prediction of cardiovascular risk [145]; prediction of the development of pregnancy-induced hypertensive disorders [146]; diagnosis of Alzheimer's disease [147]; diagnosis of benign focal liver disease [148]; prediction of metabolic syndrome [149]; AIDS research and diagnosis [150]; Parkinsonian tremor [151]; urologic oncology [152]; diagnosis of pigmented skin lesions [153]; lung nodule detection [154]; prediction of outcome in epilepsy surgery [155]; and in assisting in making diagnosis decisions in emergency room [156].…”

A Self-Learning Fuzzy Discrete Event System for HIV/AIDS Treatment Regimen Selection

“…Unlike the neural networks methodology [16], [36], which is a "black-box" approach from input-output standpoint, the reasoning chain between the input variables and the final decision in our approach is easily understandable to humans. All the parameters of the system have clear and intuitive meanings.…”