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General General

DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.

In Journal of cheminformatics

In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug-target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug-target interactions graph with two other complementary graphs namely: drug-drug similarity, target-target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug-drug similarities and target-target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

Thafar Maha A, Olayan Rawan S, Ashoor Haitham, Albaradei Somayah, Bajic Vladimir B, Gao Xin, Gojobori Takashi, Essack Magbubah

2020-Jun-29

Bioinformatics, Cheminformatics, Drug repositioning, Drug–target interaction, Graph embedding, Heterogenous network, Machine learning, Similarity integration, Similarity-based

General General

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review.

In Brain sciences

BACKGROUND : Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015.

OBJECTIVES : We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic.

METHODS : We have queried four scientific search engines (Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were extracted from each paper such as the database used, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used, number of layers implemented and which optimization approach were used as well. All these items were then investigated one by one to uncover trends.

RESULTS : Our investigation reveals that Electroencephalography (EEG) has been the most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy, while spatial-temporal features are the most used with 33.33% of the cases investigated. The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a good compromise between the complexity of the network and computational efficiency.

SIGNIFICANCE : To give useful information to the scientific community, we make our summary table of hDL-based BCI papers available and invite the community to published work to contribute to it directly. We have indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is necessary to better explore the frequency and temporal-frequency features of the data at hand.

Alzahab Nibras Abo, Apollonio Luca, Di Iorio Angelo, Alshalak Muaaz, Iarlori Sabrina, Ferracuti Francesco, Monteriù Andrea, Porcaro Camillo

2021-Jan-08

Brain-Computer Interface (BCI), Electroencephalography (EEG), Hybrid Deep Learning, Neural Networks, review, survey

General General

Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images.

In Signal, image and video processing

The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

Kc Kamal, Yin Zhendong, Wu Mingyang, Wu Zhilu

2021-Jan-07

COVID-19, Chest X-ray, Deep convolution neural network, SARS, Transfer learning

General General

spyrmsd: symmetry-corrected RMSD calculations in Python.

In Journal of cheminformatics

Root mean square displacement (RMSD) calculations play a fundamental role in the comparison of different conformers of the same ligand. This is particularly important in the evaluation of protein-ligand docking, where different ligand poses are generated by docking software and their quality is usually assessed by RMSD calculations. Unfortunately, many RMSD calculation tools do not take into account the symmetry of the molecule, remain difficult to integrate flawlessly in cheminformatics and machine learning pipelines-which are often written in Python-or are shipped within large code bases. Here we present a new open-source RMSD calculation tool written in Python, designed to be extremely lightweight and easy to integrate into existing software.

Meli Rocco, Biggin Philip C

2020-Aug-31

Python, RMSD, Software, Symmetry

General General

A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions.

In Journal of cheminformatics

Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.

Jarada Tamer N, Rokne Jon G, Alhajj Reda

2020-Jul-22

Computational drug repositioning, Data mining, Drug repositioning strategies, Machine learning, Network analysis

General General

Predicting liver cytosol stability of small molecules.

In Journal of cheminformatics

Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug discovery process. Few data exist on cytosolic enzyme-mediated metabolism and no reliable tools are available to chemists to help design away from such liabilities. In this study, we screened 1450 compounds for liver cytosol-mediated metabolic stability and extracted transformation rules that might help medicinal chemists in optimizing compounds with these liabilities. In vitro half-life data were collected by performing in-house experiments in mouse (CD-1 male) and human (mixed gender) cytosol fractions. Matched molecular pairs analysis was performed in conjunction with qualitative-structure activity relationship modeling to identify chemical structure transformations affecting cytosolic stability. The transformation rules were prospectively validated on the test set. In addition, selected rules were validated on a diverse chemical library and the resulting pairs were experimentally tested to confirm whether the identified transformations could be generalized. The validation results, comprising nearly 250 library compounds and corresponding half-life data, are made publicly available. The datasets were also used to generate in silico classification models, based on different molecular descriptors and machine learning methods, to predict cytosol-mediated liabilities. To the best of our knowledge, this is the first systematic in silico effort to address cytosolic enzyme-mediated liabilities.

Shah Pranav, Siramshetty Vishal B, Zakharov Alexey V, Southall Noel T, Xu Xin, Nguyen Dac-Trung

2020-Apr-07

Cytosol stability, Machine learning, Matched molecular pairs, Qualitative-structure activity relationship, Xenobiotic metabolism