Efficient machine learning model for predicting drug-target interactions with case study for Covid-19

El-Behery, Heba; Attia, Abdel-Fattah; El-Fishawy, Nawal; Torkey, Hanaa

doi:10.1016/j.compbiolchem.2021.107536

Cited by 32 publications

(22 citation statements)

References 28 publications

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“…Therefore, methods for an in-few-seconds assessment of possible evaluation (feasibility with respect to, e.g., the docking score evaluation) of large compound sets need to be developed. This is indeed a well-suited task for machine learning (ML) protocols [23] , [46] , [47] . One of the promising machine learning techniques is Deep Tensor Neural Networks (DTNNs) implemented in the SchNetPack package [48] .…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction of 3CL SARS-CoV-2 docking scores

Bučinský

Bortňák

Gall

et al. 2022

Computational Biology and Chemistry

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Section: Introductionmentioning

confidence: 99%

Machine learning prediction of 3CL SARS-CoV-2 docking scores

Bučinský

Bortňák

Gall

et al. 2022

Computational Biology and Chemistry

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“…After collecting the known drug- and disease-related features data (DTIs), we assumed them to be positive samples, and the unknown interactions achieved through the randomized shuffling of the positive samples were assumed to be negative samples ( 63 ) followed by upsampling of the negative samples to create a balanced dataset. The rationale behind this is that conventional methods of unknown interactions between targeted drugs as negative examples may result in bias because unknown interactions between targeted drugs may contain undetected interactions between the targeted drugs.…”

Section: Methodsmentioning

confidence: 99%

“…For bioinformatics research, machine learning plays a significant role in the filtering and comprehension of patterns in a given dataset ( 63 ). Our proposed study presents machine-learning models trained on various aspects of the drugs and disease data.…”

Section: Methodsmentioning

confidence: 99%

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19

Ahmed

Lee

Samantasinghar

et al. 2022

Front. Public Health

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The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

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“…From a machine learning performance point of view, we compare the DTI prediction performance between SAE-DNN and other deep learning models implemented in research [44,45]. Although these studies used a binary approach to predict DTI, comparisons can be made by looking at the model's performance in predicting positive classes.…”

Section: Comparison With Other Approaches From the Literaturementioning

confidence: 99%

“…Therefore, the comparison is done using recall and f-measure metrics. SAE-DNN outperforms other deep learning such as standard artificial neural network (ANN) and deep belief network (DBN) method from Research [44] with the best f-measure of 0.89368 compared to standard ANN with f-measure of 0.88 and DBN with an f-measure of 0.885. SAE-DNN also outperforms the proposed ComboNet method [45] with the best recall of 0.918 compared to the ComboNet recall of 0.8.…”

Section: Comparison With Other Approaches From the Literaturementioning

confidence: 99%

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Fadli

Kusuma

Annisa

et al. 2021

BDCC

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Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug repurposing can be done by observing the interactions of drug compounds with protein related to a disease (DTI), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm. Compound features were extracted using PubChem fingerprint, daylight fingerprint, MACCS fingerprint, and circular fingerprint. The results showed that the SAE-DNN model was able to predict DTI in COVID-19 cases with good performance. The SAE-DNN model with a circular fingerprint dataset produced the best average metrics with an accuracy of 0.831, recall of 0.918, precision of 0.888, and F-measure of 0.89. Herbal compounds prediction results using the SAE-DNN model with the circular, daylight, and PubChem fingerprint dataset resulted in 92, 65, and 79 herbal compounds contained in herbal plants in Indonesia respectively.

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Efficient machine learning model for predicting drug-target interactions with case study for Covid-19

Abstract: Graphical abstract

Cited by 32 publications

References 28 publications

Machine learning prediction of 3CL SARS-CoV-2 docking scores

Machine learning prediction of 3CL SARS-CoV-2 docking scores

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

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