Descriptive prediction of drug side‐effects using a hybrid deep learning model

Lee, Chun Yen; Chen, Yi‐Ping Phoebe

doi:10.1002/int.22389

Cited by 33 publications

(5 citation statements)

References 37 publications

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“…Furthermore, Chun Yen Lee et.al. [20] proposed a hybrid deep learning model, this proposed model consists of a graph convolutional neural network (GCNN) model and bidirectional long short-term memory (Bi-LSTM) for efficient learning of drug features and association of DSEs. Giovanna Maria Dimitri and Pietro Lió [21] proposed a machine learning-based model under the name DrugClust.…”

Section: A Nlp-based Dses Predictionmentioning

confidence: 99%

Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images

Arshed,

Ibrahim,

Mumtaz

et al. 2024

IEEE Access

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Drug Side Effects (DSE) are inconvenient and inadvertent retorts of the drugs. DSEs impact on public health and healthcare can prove costly. These DSEs can be an important factor in the failure/acceptance of drugs. Every approved drug should be either free from DSEs or these should be minor and reported properly. The drug discovery process should be capable of predicting and preventing these effects in advance. Previously proposed studies for the prediction/prevention of DSEs utilized the features of 1D drug chemical structures or Natural Language Processing (NLP). Both these techniques required a complex transformation process. In this research authors have proposed a deep learning model, specifically using a transfer learning approach to predict DSEs directly from 2D chemical structure images, eliminating the need for the hefty transformation process of the NLP domain. for this study, a unique dataset is prepared that associates each image (taken from PubChem) with its specific side effects (SIDER). The results are evaluated using Accuracy, Precision, Recall and F-measure. The proposed model showed its dominance with an Accuracy of 73%, Precision of 83%, Recall of 73%, and an F1 score of 75%. The achieved results of the proposed model are compared against established transfer learning models like VGG16, DenseNet121 and some previously used traditional machine learning models like SVM and KNN. The collected results indicate a significant advancement in predicting drug side effects and offer a promising avenue for streamlining the drug development process.

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Section: A Nlp-based Dses Predictionmentioning

confidence: 99%

Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images

Arshed,

Ibrahim,

Mumtaz

et al. 2024

IEEE Access

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“…Hybrid methods have also proven to be effective. An example of one is the trained DL model for drug side-effect prediction and description, consisting of a graph CNN with Inception modules, and a BiLSTM word-embedding layer [52]. The GCNN is used to predict drug relationships by autoencoding drug names that are converted into word vectors via the word-embedding layer.…”

Section: Related Workmentioning

confidence: 99%

Modeling of the Crystallization Conditions for Organic Synthesis Product Purification Using Deep Learning

2022

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Crystallization is an important purification technique for solid products in a chemical laboratory. However, the correct selection of a solvent is important for the success of the procedure. In order to accelerate the solvent or solvent mixture search process, we offer an in silico alternative, i.e., a never previously demonstrated approach that can model the reaction mixture crystallization conditions which are invariant to the reaction type. The offered deep learning-based method is trained to directly predict the solvent labels used in the crystallization steps of the synthetic procedure. Our solvent label prediction task is a multi-label multi-class classification task during which the method must correctly choose one or several solvents from 13 possible examples. During the experimental investigation, we tested two multi-label classifiers (i.e., Feed-Forward and Long Short-Term Memory neural networks) applied on top of vectors. For the vectorization, we used two methods (i.e., extended-connectivity fingerprints and autoencoders) with various parameters. Our optimized technique was able to reach the accuracy of 0.870 ± 0.004 (which is 0.693 above the baseline) on the testing dataset. This allows us to assume that the proposed approach can help to accelerate manual R&D processes in chemical laboratories.

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“…Since 2012, when AlexNet 13 won the 2012 ILSVRC competition, 14 numerous important breakthroughs in computer vision have been achieved using DCNNs 15‐20 . Benefit from the development of DCNNs, continuous optimization of object detection algorithms in natural images, and the release of open‐source medical image datasets, the studies on object detection in medical images have made significant progress.…”

Section: Related Workmentioning

confidence: 99%

“…Since 2012, when AlexNet 13 won the 2012 ILSVRC competition, 14 numerous important breakthroughs in computer vision have been achieved using DCNNs. [15][16][17][18][19][20] Benefit from the development of DCNNs, continuous optimization of object detection algorithms in natural images, and the release of open-source medical image datasets, the studies on object detection in medical images have made significant progress. According to the different dimensions of the medical data used in them, these studies can be divided into two-dimensional (2D) detection and three-dimensional (3D) detection.…”

Section: Object Detection In Medical Imagesmentioning

confidence: 99%

An intelligent system of pelvic lymph node detection

et al. 2021

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Computed tomography (CT) scanning is a fast and painless procedure that can capture clear imaging information beneath the abdomen and is widely used to help diagnose and monitor disease progress. The pelvic lymph node is a key indicator of colorectal cancer metastasis. In the traditional process, an experienced radiologist must read all the CT scanning images slice by slice to track the lymph nodes for future diagnosis. However, this process is time‐consuming, exhausting, and subjective due to the complex pelvic structure, numerous blood vessels, and small lymph nodes. Therefore, automated methods are desirable to make this process easier. Currently, the available open‐source CTLNDataset only contains large lymph nodes. Consequently, a new data set called PLNDataset, which is dedicated to lymph nodes within the pelvis, is constructed to solve this issue. A two‐level annotation calibration method is proposed to guarantee the quality and correctness of pelvic lymph node annotation. Moreover, a novel system composed of a keyframe localization network and a lymph node detection network is proposed to detect pelvic lymph nodes in CT scanning images. The proposed method makes full use of two kinds of prior knowledge: spatial prior knowledge for keyframe localization and anchor prior knowledge for lymph node detection. A series of experiments are carried out to evaluate the proposed method, including ablation experiments, comparing other state‐of‐the‐art methods, and visualization of results. The experimental results demonstrate that our proposed method outperforms other methods on PLNDataset and CTLNDataset. This system is expected to be applied in future clinical practice.

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Descriptive prediction of drug side‐effects using a hybrid deep learning model

Cited by 33 publications

References 37 publications

Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images

Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images

Modeling of the Crystallization Conditions for Organic Synthesis Product Purification Using Deep Learning

An intelligent system of pelvic lymph node detection

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