scholarly journals Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 128
Author(s):  
Maged Nasser ◽  
Naomie Salim ◽  
Hentabli Hamza ◽  
Faisal Saeed ◽  
Idris Rabiu
Keyword(s):  
Deep Learning ◽  
Molecular Similarity ◽  
Similarity Searching ◽  
Reference Structure ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Molecular Features ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Similarity Method

Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is used to identify molecules that have similarities to a user-defined reference structure and is evaluated by quantitative measures of intermolecular structural similarity. Among existing approaches, 2D fingerprints are widely used. The similarity of a reference structure and a database structure is measured by the computation of association coefficients. In most classical similarity approaches, it is assumed that the molecular features in both biological and non-biologically-related activity carry the same weight. However, based on the chemical structure, it has been found that some distinguishable features are more important than others. Hence, this difference should be taken consideration by placing more weight on each important fragment. The main aim of this research is to enhance the performance of similarity searching by using multiple descriptors. In this paper, a deep learning method known as deep belief networks (DBN) has been used to reweight the molecule features. Several descriptors have been used for the MDL Drug Data Report (MDDR) dataset each of which represents different important features. The proposed method has been implemented with each descriptor individually to select the important features based on a new weight, with a lower error rate, and merging together all new features from all descriptors to produce a new descriptor for similarity searching. Based on the extensive experiments conducted, the results show that the proposed method outperformed several existing benchmark similarity methods, including Bayesian inference networks (BIN), the Tanimoto similarity method (TAN), adapted similarity measure of text processing (ASMTP) and the quantum-based similarity method (SQB). The results of this proposed multi-descriptor-based on Stack of deep belief networks method (SDBN) demonstrated a higher accuracy compared to existing methods on structurally heterogeneous datasets.

Features Reweighting and Selection in ligand-based Virtual Screening for Molecular Similarity Searching Based on Deep Belief Networks

2021 ◽  
pp. 2050009
Author(s):  
Maged Nasser ◽  
Naomie Salim ◽  
Hentabli Hamza ◽  
Faisal Saeed ◽  
Idris Rabiu
Keyword(s):  
Virtual Screening ◽  
Error Rate ◽  
Principal Component ◽  
Heterogeneous Data ◽  
Similarity Searching ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Prior Probabilities ◽  
Molecular Features ◽  
Feature Error

Virtual screening (VS) is defined as the use of a compilation of computational procedures to grade, score and/or sort several chemical formations. The purpose of VS is to identify the molecules holding the greatest prior probabilities of activity. Many of the conventional similarity methods assume that molecular features that do not relate to the biological activity carry the same weight as the important ones. For this reason, the researchers on this paper investigated that some features are being more important than others through the chemist structure diagrams and the weight for each fragment should be taken into consideration by giving more weight to those fragments that are more important. In this paper, a deep learning method specifically known as Deep Belief Networks (DBN) has been used to reweight the molecule features and based on this new weigh, the reconstruction feature error has been calculated for all the features. Based on the reconstruction feature error values, Principal Component Analysis (PCA) has been used for the dimension’s reduction and only few hundreds of features have been selected based on the less error rate. The main aim of this research is to show an improvement of the similarity searching performance based on the selected features those have less error rate. The results derived through the DBN were compared with those derived through other similarity methods, such as the Tanimoto coefficient and the quantum-based methods. This comparison revealed the performance of the DBN with the structurally heterogeneous data sets (DS1 and DS3) to be superior to the performances of all the other techniques.

scholarly journals Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Xu ◽  
Yan jun Fang ◽  
Dong Wang ◽  
Jia qi Liang ◽  
Kwok Leung Tsui
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Roller Bearings ◽  
Mode Decomposition

Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C-means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition- (VMD-) singular value decomposition- (SVD-) FCM, and ensemble empirical mode decomposition- (EEMD-) fuzzy entropy- (FE-) PCA-FCM.

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease

2016 ◽  
Vol 26 (07) ◽  
pp. 1650025 ◽  
Author(s):  
Andrés Ortiz ◽  
Jorge Munilla ◽  
Juan M. Górriz ◽  
Javier Ramírez
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Brain Area ◽  
Brain Regions ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Learning Architectures ◽  
Aided Diagnosis

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.

scholarly journals Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks

2020 ◽  
Vol 24 (6) ◽  
pp. 1805-1813 ◽  
Author(s):  
Jun Ying ◽  
Joyita Dutta ◽  
Ning Guo ◽  
Chenhui Hu ◽  
Dan Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Exacerbation Frequency

DLSCORE: A Deep Learning Model for Predicting Protein-Ligand Binding Affinities

2018 ◽  
Author(s):  
Mahmudulla Hassan ◽  
Daniel Castaneda Mogollon ◽  
Olac Fuentes ◽  
suman sirimulla
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ligand Binding ◽  
Scoring Function ◽  
Learning Approaches ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Fully Connected

<p>In recent years, the cheminformatics community has seen an increased success with machine learning-based scoring functions for estimating binding affinities and pose predictions. The prediction of protein-ligand binding affinities is crucial for drug discovery research. Many physics-based scoring functions have been developed over the years. Lately, machine learning approaches are proven to boost the performance of traditional scoring functions. In this study, a novel deep learning based scoring function (DLSCORE) was developed and trained on the refined PDBBind v.2016 dataset using 348 BINding ANAlyzer (BINANA) descriptors. The neural networks of the DLSCORE model have different number of fully connected hidden layers. Our model, an ensemble of 10 networks, yielded a Pearson R2 of 0.82, a Spearman Rho R2 of 0.90, Kendall Tau R2 of 0.74, an RMSE of 1.15 kcal=mol, and an MAE of 0.86 kcal=mol for our test set. This software is available on Github at https://github.com/sirimullalab/dlscore.git</p><p><br></p>

DLSCORE: A Deep Learning Model for Predicting Protein-Ligand Binding Affinities

2018 ◽  
Author(s):  
Mahmudulla Hassan ◽  
Daniel Castaneda Mogollon ◽  
Olac Fuentes ◽  
suman sirimulla
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ligand Binding ◽  
Scoring Function ◽  
Learning Approaches ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Fully Connected

<p>In recent years, the cheminformatics community has seen an increased success with machine learning-based scoring functions for estimating binding affinities and pose predictions. The prediction of protein-ligand binding affinities is crucial for drug discovery research. Many physics-based scoring functions have been developed over the years. Lately, machine learning approaches are proven to boost the performance of traditional scoring functions. In this study, a novel deep learning based scoring function (DLSCORE) was developed and trained on the refined PDBBind v.2016 dataset using 348 BINding ANAlyzer (BINANA) descriptors. The neural networks of the DLSCORE model have different number of fully connected hidden layers. Our model, an ensemble of 10 networks, yielded a Pearson R2 of 0.82, a Spearman Rho R2 of 0.90, Kendall Tau R2 of 0.74, an RMSE of 1.15 kcal=mol, and an MAE of 0.86 kcal=mol for our test set. This software is available on Github at https://github.com/sirimullalab/dlscore.git</p><p><br></p>

Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction Model

2020 ◽  
Vol 13 (3) ◽  
pp. 508-518
Author(s):  
Abderrazak Khediri ◽  
Mohamed Ridda Laouar ◽  
Sean B. Eom
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Smart Grids ◽  
Deep Belief Network ◽  
Power Grids ◽  
Economic Losses ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Belief Network ◽  
Proposed Model

Background: Enhancing the resiliency of electric power grids is becoming a crucial issue due to the outages that have recently occurred. One solution could be the prediction of imminent failure that is engendered by line contingency or grid disturbances. Therefore, a number of researchers have initiated investigations to generate techniques for predicting outages. However, extended blackouts can still occur due to the frailty of distribution power grids. Objective: This paper implements a proactive prediction model based on deep-belief networks to predict the imminent outages using previous historical blackouts, trigger alarms, and suggest solutions for blackouts. These actions can prevent outages, stop cascading failures and diminish the resulting economic losses. Methods: The proposed model is divided into three phases: A, B and C. The first phase (A) represents the initial segment that collects and extracts data and trains the deep belief network using the collected data. Phase B defines the Power outage threshold and determines whether the grid is in a normal state. Phase C involves detecting potential unsafe events, triggering alarms and proposing emergency action plans for restoration. Results: Different machine learning and deep learning algorithms are used in our experiments to validate our proposition, such as Random forest, Bayesian nets and others. Deep belief Networks can achieve 97.30% accuracy and 97.06% precision. Conclusion: The obtained findings demonstrate that the proposed model would be convenient for blackouts’ prediction and that the deep-belief network represents a powerful deep learning tool that can offer plausible results.

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