Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening

Nasser, Maged; Salim, Naomie; Hamza, Hentabli; Saeed, Faisal

doi:10.1007/978-3-319-99007-1_1

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…Deep belief networks are a type of the deep learning methods, choosing the significant characteristics to lessen the high dimensionality via stack of Restricted Boltzmann Machine and optimize to boost weights and diminish the renovated feature error. Thereby, removing the qualities that entertain extra reconstruct error and only use only less constrict error showing the experimental enhancements of results in Virtual Screening outcomes [8].…”

Section: Review Of Literaturementioning

confidence: 99%

Future Trends of the Healthcare Data Predictive Analytics using Soft Computing Techniques in Data Science

Dhamodaran¹,

Balmoor²

2019

CVRJST

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Predictive Analytics, Soft Computing (SC) and Optimization, Data Mining and Data Science are rapidly becoming some of the most-discussed, perhaps utmost glorified topics in healthcare business. Artificial Intelligence, Machine Learning, Artificial Neural Networks, Fuzzy Logic, Expert Systems, etc., is well-studied disciplines with a long history of success in many industries. Healthcare can acquire treasured sessions from this prior achievement to startup the efficacy of predictive analytics for refining patient care, chronic disease management, hospital administration and supply chain efficiencies. The prospects that presently occurs for healthcare systems is to state what "predictive analytics" stands for to them and how can it be cast off furthermost excellently to cause further enhancements.In all industries including healthcare, prediction plays a best worthwhile role when that data is passed on as accomplishments. The inclinations to mediate the vital data is in harnessing the power of historical and real-time data with visions from forecasting those data based on the times ahead. Importantly, to best gauge efficacy and value, both the predictor and the intervention must be integrated within the same system and workflow where the trend occurs.A valuable report of the organized publicity and expectation of predictive analytics in healthcare through a blend of psychology, digital technology, and entrepreneurship is available for real-time implementation for the good of the public. Review and evaluation on these disciplines pave ways to open up new arenas envisaging the future trends of Predictive analytics, Data Mining and Science and Soft Computing (SC) in healthcare, stepping strongly into pervasive computing, ambient intelligence, ubiquitous computing and many more automated technical concepts and computing's ahead.

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Section: Review Of Literaturementioning

confidence: 99%

Future Trends of the Healthcare Data Predictive Analytics using Soft Computing Techniques in Data Science

Dhamodaran¹,

Balmoor²

2019

CVRJST

View full text Add to dashboard Cite

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“…Here, we evaluate the search methods for similarity by using MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) data sets, which are the most common [ 30 , 31 ]. The MDDR datasets have been used by our research group and previous studies [ 3 , 4 , 6 , 9 , 10 , 19 , 20 , 21 , 22 , 25 , 26 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. All molecules have been translated to ECFC-4 fingerprint by the Pipeline Pilot software, and our study community has used these databases.…”

Section: Experimental Designmentioning

confidence: 99%

“…The effectiveness of the proposed approaches is assessed as follows: The recall metric, which is the part of active chemical compounds that can be identified inside the top 1 and 5% of the ranking test set, is the first method for assessing the retrieval model’s performance. This metric has already been utilized in research [ 3 , 4 , 6 , 9 , 10 , 19 , 20 , 21 , 22 , 25 , 26 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Figure 6 shows the general steps of the experimental design of this study.…”

Section: Experimental Designmentioning

confidence: 99%

“… The Kendall W is the third significant measure that may be used to evaluate the suggested procedures, often known as the significance test. This significance test has already been utilized in prior research [ 3 , 4 , 9 , 10 , 19 , 20 , 21 , 22 , 25 , 26 , 29 , 32 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. This test can be construed as a measure of rater agreement.…”

Section: Experimental Designmentioning

confidence: 99%

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Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual Screen

Altalib

Salim

2022

Biomolecules

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Information technology has become an integral aspect of the drug development process. The virtual screening process (VS) is a computational technique for screening chemical compounds in a reasonable amount of time and cost. The similarity search is one of the primary tasks in VS that estimates a molecule’s similarity. It is predicated on the idea that molecules with similar structures may also have similar activities. Many techniques for comparing the biological similarity between a target compound and each compound in the database have been established. Although the approaches have a strong performance, particularly when dealing with molecules with homogenous active structural, they are not enough good when dealing with structurally heterogeneous compounds. The previous works examined many deep learning methods in the enhanced Siamese similarity model and demonstrated that the Enhanced Siamese Multi-Layer Perceptron similarity model (SMLP) and the Siamese Convolutional Neural Network-one dimension similarity model (SCNN1D) have good outcomes when dealing with structurally heterogeneous molecules. To further improve the retrieval effectiveness of the similarity model, we incorporate the best two models in one hybrid model. The reason is that each method gives good results in some classes, so combining them in one hybrid model may improve the retrieval recall. Many designs of the hybrid models will be tested in this study. Several experiments on real-world data sets were conducted, and the findings demonstrated that the new approaches outperformed the previous method.

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Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

et al. 2022

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The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP), and Quantum-Based Similarity Method (SQB). The results demonstrated that the performance achieved by the proposed approach has proven to be superior, particularly with the use of structurally heterogeneous datasets, where it yielded improved results compared to other previously used methods with the similar goal of improving molecular similarity searching.

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Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening

Cited by 5 publications

References 30 publications

Future Trends of the Healthcare Data Predictive Analytics using Soft Computing Techniques in Data Science

Future Trends of the Healthcare Data Predictive Analytics using Soft Computing Techniques in Data Science

Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual Screen

Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

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