scholarly journals Prediction of Drug-likeness of Central Nervous System Drug Candidates Using a Feed-Forward Neural Network Based on Chemical Structure

2020 ◽  
Author(s):  
Yi-Gao Yuan ◽  
Xiao Wang
Keyword(s):  
Neural Network ◽  
Central Nervous System ◽  
Nervous System ◽  
Chemical Structure ◽  
Early Stage ◽  
Medical Science ◽  
Well Being ◽  
New Drugs ◽  
Feed Forward Neural Network ◽  
Feed Forward

Modern medical science has been greatly advanced by the development of new drugs, despite the fact that the process of developing new drugs is costly and time-consuming. An accurate prediction method for the drug-likeness in the early stage of drug discovery is highly desirable, as it will facilitate the discovery process and reduce the overall cost and eventually contribute to human well-being. Based on a central nervous system (CNS) drug dataset, we constructed an artificial neural network (NN) to predict the CNS drug-likeness of a given bioactive compound. We first constructed a simple feed-forward neural network, to learn and predict the possible correlations between twelve physiochemical properties and the CNS drug-likeness. The accuracy of prediction has reached 80%, which has been improved from previous reports. We further constructed a neural network based on chemical structure, and the accuracy has increased to 86%. The successful prediction of the CNS drug-likeness renders this NN a powerful tool for virtual drug screening. <br>

scholarly journals Prediction of Drug-likeness of Central Nervous System Drug Candidates Using a Feed-Forward Neural Network Based on Chemical Structure

2020 ◽  
Author(s):  
Yi-Gao Yuan ◽  
Xiao Wang
Keyword(s):  
Neural Network ◽  
Central Nervous System ◽  
Nervous System ◽  
Chemical Structure ◽  
Early Stage ◽  
Medical Science ◽  
Well Being ◽  
New Drugs ◽  
Feed Forward Neural Network ◽  
Feed Forward

Modern medical science has been greatly advanced by the development of new drugs, despite the fact that the process of developing new drugs is costly and time-consuming. An accurate prediction method for the drug-likeness in the early stage of drug discovery is highly desirable, as it will facilitate the discovery process and reduce the overall cost and eventually contribute to human well-being. Based on a central nervous system (CNS) drug dataset, we constructed an artificial neural network (NN) to predict the CNS drug-likeness of a given bioactive compound. We first constructed a simple feed-forward neural network, to learn and predict the possible correlations between twelve physiochemical properties and the CNS drug-likeness. The accuracy of prediction has reached 80%, which has been improved from previous reports. We further constructed a neural network based on chemical structure, and the accuracy has increased to 86%. The successful prediction of the CNS drug-likeness renders this NN a powerful tool for virtual drug screening. <br>

scholarly journals Prediction of Drug-likeness of Central Nervous System Drug Candidates Using a Feed-Forward Neural Network Based on Chemical Structure

2020 ◽  
Author(s):  
Yi-Gao Yuan ◽  
Xiao Wang
Keyword(s):  
Neural Network ◽  
Central Nervous System ◽  
Nervous System ◽  
Chemical Structure ◽  
Well Being ◽  
New Drugs ◽  
Successful Implementation ◽  
Human Beings ◽  
Feed Forward Neural Network ◽  
Feed Forward

The modern medical science has been greatly advanced by the development of new drugs, despite the fact that the process of developing new drugs is costly and time-consuming. An accurate prediction method for the drug-likeness at the early stage of drug discovery is highly desirable, as it will facilitate the discovery process and reduce the overall cost, and eventually contribute to the well-being of human beings. Based on a central nervous system (CNS) drug dataset, we constructed an artificial neural network (NN) to predict the CNS drug-likeness of a given compound. Based on the published results, we first constructed a simple feed-forward neural network to learn and predict the possible correlations between twelve physiochemical properties and the CNS drug-likeness. The accuracy of prediction has reached 80%, which is higher than previous reports. The successful implementation of NN to predict the CNS drug-likeness indicated that NN could be a powerful tool for the prediction. Moreover, we further constructed a neural network based on the chemical structure, and the accuracy has reached 86%. We hope that these methods can serve as an applicable set of protocols for virtual drug screening.

scholarly journals Parkinson‟s Disease Prediction using Modified Gauss-Newton Method in Feed-Forward Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 4986-4993
Keyword(s):  
Neural Network ◽  
Newton Method ◽  
Early Stage ◽  
Optimization Techniques ◽  
The Body ◽  
Probabilistic Methods ◽  
Weight Optimization ◽  
Feed Forward Neural Network ◽  
Brain Disorder ◽  
Feed Forward

Parkinson’s disease (PD) is a brain disorder, characterized by the relapse of the nervous system that spreads gradually in the body. The symptom of PD includes a loss of body control (moderate movement, resting tremors, postural shakiness etc.). So, it is required to detect at an early stage. Machine learning (ML) deals with a variety of probabilistic methods to identify a pattern in a dataset. Therefore, the research is carried out to predict the PD using Multilayer Feed-Forward Neural Network. In Neural Network (NN), weight optimization performed at each layer that plays a major role in the prediction. First-order weight optimization techniques are slow in computation because they reduce the sum of square error using parameter updating in the steepest descent way. In proposed work, a modified recursive Gauss-Newton method is used to optimize the weights for speed up the performance of Feed-Forward NN. This approach is compared with widely used optimization techniques. The Proposed method found better than other techniques and performs fast in Apache Spark than R-Studio framework.

scholarly journals A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ

2021 ◽  
Vol 118 ◽  
pp. 103766
Author(s):  
Ahmed J. Aljaaf ◽  
Thakir M. Mohsin ◽  
Dhiya Al-Jumeily ◽  
Mohamed Alloghani
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Feed Forward Neural Network ◽  
Feed Forward

scholarly journals Neurological and mental health consequences of COVID-19: potential implications for well-being and labor force

2021 ◽  
Author(s):  
I B Meier ◽  
C Vieira Ligo Teixeira ◽  
I Tarnanas ◽  
F Mirza ◽  
L Rajendran
Keyword(s):  
Mental Health ◽  
Central Nervous System ◽  
Nervous System ◽  
Cognitive Impairment ◽  
Immune Cell ◽  
Previous History ◽  
Well Being ◽  
Acute Disseminated Encephalomyelitis ◽  
The Central Nervous System ◽  
Neurological Protection

Abstract Recent case studies show that the SARS-CoV-2 infectious disease, COVID-19, is associated with accelerated decline of mental health, in particular, cognition in elderly individuals, but also with neurological and neuropsychiatric illness in young people. Recent studies also show a bidirectional link between COVID-19 and mental health in that people with previous history of psychiatric illness have a higher risk for contracting COVID-19 and that COVID-19 patients display a variety of psychiatric illnesses. Risk factors and the response of the central nervous system to the virus show large overlaps with pathophysiological processes associated with Alzheimer’s disease, delirium, post-operative cognitive dysfunction and acute disseminated encephalomyelitis, all characterized by cognitive impairment. These similarities lead to the hypothesis that the neurological symptoms could arise from neuroinflammation and immune cell dysfunction both in the periphery as well as in the central nervous system and the assumption that long-term consequences of COVID-19 may lead to cognitive impairment in the well-being of the patient and thus in today’s workforce, resulting in large loss of productivity. Therefore, particular attention should be paid to neurological protection during treatment and recovery of COVID-19, while cognitive consequences may require monitoring.

scholarly journals Development of the Cerebrospinal Fluid in Early Stage after Hemorrhage in the Central Nervous System

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 300
Author(s):  
Petr Kelbich ◽  
Aleš Hejčl ◽  
Jan Krejsek ◽  
Tomáš Radovnický ◽  
Inka Matuchová ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Cerebrospinal Fluid ◽  
Nervous System ◽  
Early Stage ◽  
Rank Test ◽  
Signed Rank ◽  
Signed Rank Test ◽  
The Central Nervous System ◽  
Poor Outcomes ◽  
Molecular Patterns

Extravasation of blood in the central nervous system (CNS) represents a very strong damaged associated molecular patterns (DAMP) which is followed by rapid inflammation and can participate in worse outcome of patients. We analyzed cerebrospinal fluid (CSF) from 139 patients after the CNS hemorrhage. We compared 109 survivors (Glasgow Outcome Score (GOS) 5-3) and 30 patients with poor outcomes (GOS 2-1). Statistical evaluations were performed using the Wilcoxon signed-rank test and the Mann–Whitney U test. Almost the same numbers of erythrocytes in both subgroups appeared in days 0–3 (p = 0.927) and a significant increase in patients with GOS 2-1 in days 7–10 after the hemorrhage (p = 0.004) revealed persistence of extravascular blood in the CNS as an adverse factor. We assess 43.3% of patients with GOS 2-1 and only 27.5% of patients with GOS 5-3 with low values of the coefficient of energy balance (KEB < 15.0) in days 0–3 after the hemorrhage as a trend to immediate intensive inflammation in the CNS of patients with poor outcomes. We consider significantly higher concentration of total protein of patients with GOS 2-1 in days 0–3 after hemorrhage (p = 0.008) as the evidence of immediate simultaneously manifested intensive inflammation, swelling of the brain and elevation of intracranial pressure.

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