A Convolutional Neural Network for Virtual Screening of Molecular Fingerprints

Mendolia, Isabella; Contino, Salvatore; Perricone, Ugo; Pirrone, Roberto; Ardizzone, Edoardo

doi:10.1007/978-3-030-30642-7_36

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…39 Also, Mendolia et al used convolutional neural networks (CNNs), which are intended to identify a set of candidate compounds for a specific target protein in terms of their biological activity, and both 1D and 2D CNNs were trained separately to test the performance of every single descriptor. 40 Moreover, several researchers have proposed to exploit RNNbased methods for chemoinformatics. The majority of the researchers have utilized the model as a prediction or classifier model.…”

Section: Related Workmentioning

confidence: 99%

“…Hamza et al used CNN to determine its precision during the prediction of orphan compound activities . Also, Mendolia et al used convolutional neural networks (CNNs), which are intended to identify a set of candidate compounds for a specific target protein in terms of their biological activity, and both 1D and 2D CNNs were trained separately to test the performance of every single descriptor . Moreover, several researchers have proposed to exploit RNN-based methods for chemoinformatics.…”

Section: Related Workmentioning

confidence: 99%

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Similarity-Based Virtual Screen Using Enhanced Siamese Deep Learning Methods

Altalib

Salim

2022

ACS Omega

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Traditional drug production is a long and complex process that leads to new drug production. The virtual screening technique is a computational method that allows chemical compounds to be screened at an acceptable time and cost. Several databases contain information on various aspects of biologically active substances. Simple statistical tools are difficult to use because of the enormous amount of information and complex data samples of molecules that are structurally heterogeneous recorded in these databases. Many techniques for capturing the biological similarity between a test compound and a known target ligand in LBVS have been established. However, despite the good performances of the above methods compared to their prior, especially when dealing with molecules that have homogeneous active structural elements, they are not satisfied when dealing with molecules that are structurally heterogeneous. Deep learning models have recently achieved considerable success in a variety of disciplines due to their powerful generalization and feature extraction capabilities. Also, the Siamese network has been used in similarity models for more complicated data samples, especially with heterogeneous data samples. The main aim of this study is to enhance the performance of similarity searching, especially with molecules that are structurally heterogeneous. The Siamese architecture will be enhanced using two similarity distance layers with one fusion layer to further improve the similarity measurements between molecules and then adding many layers after the fusion layer for some models to improve the retrieval recall. In this architecture, several methods of deep learning have been used, which are long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network-one dimension (CNN1D), and convolutional neural network-two dimensions (CNN2D). A series of experiments have been carried out on real-world data sets, and the results have shown that the proposed methods outperformed the existing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%