Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks

Dhami, Devendra Singh; Yan, Siwen; Kunapuli, Gautam; Page, David L.; Natarajan, Sriraam

doi:10.48550/arxiv.1911.06356

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the drug discovery domain, Dhami et al was using images as an input to predict drug interactions in a Siamese convolution network architecture. 46 Jeon et al proposed a method to use MLP Siamese neural networks (ReSimNet) in structure-based virtual screening (SBVS) to calculate the distance by cosine similarity. 22 Despite the good performances of the above methods compared to their prior, especially when dealing with molecules that have homogeneous active structural elements like classes of molecules in the MDL Drug Data Report data set MDDR_DR2, however, the performances are not satisfied when dealing with molecules of structurally heterogeneous nature like classes of molecules in the MDL Drug Data Report data set MDDR_DR3 and Maximum Unbiased Validation (MUV) data set.…”

Section: Related Workmentioning

confidence: 99%

Similarity-Based Virtual Screen Using Enhanced Siamese Deep Learning Methods

Altalib

Salim

2022

ACS Omega

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%