Deep learning application detecting SARS-CoV-2 key enzymes inhibitors

Benarous, Leila; Benarous, Khedidja; Muhammad, Ghulam; Ali, Zulfiqar

doi:10.1007/s10586-022-03656-6

Cited by 3 publications

(1 citation statement)

References 63 publications

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“…Another effort towards designing and developing AI-driven SARS-CoV-2 drugs is the CoronaDB-AI database of natural compounds, proteins, and amino acids, which can train models to rapidly discover effective drugs and target enzyme inhibitors [16,22]. ML-based molecular docking is a primary step toward discovering virtual drugs, demanding the analysis of the molecular structure and chemical bonding of the target molecules.…”

Section: Artificial Intelligence In Biosensor Synthesismentioning

confidence: 99%

Prospects of Using Machine Learning and Diamond Nanosensing for High Sensitivity SARS-CoV-2 Diagnosis

2023

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The worldwide death toll claimed by Acute Respiratory Syndrome Coronavirus Disease 2019 (SARS-CoV), including its prevailed variants, is 6,812,785 (worldometer.com accessed on 14 March 2023). Rapid, reliable, cost-effective, and accurate diagnostic procedures are required to manage pandemics. In this regard, we bring attention to quantum spin magnetic resonance detection using fluorescent nanodiamonds for biosensing, ensuring the benefits of artificial intelligence-based biosensor design on an individual patient level for disease prediction and data interpretation. We compile the relevant literature regarding fluorescent nanodiamonds-based SARS-CoV-2 detection along with a short description of viral proliferation and incubation in the cells. We also propose a potentially effective strategy for artificial intelligence-enhanced SARS-CoV-2 biosensing. A concise overview of the implementation of artificial intelligence algorithms with diamond magnetic nanosensing is included, covering this roadmap’s benefits, challenges, and prospects. Some mutations are alpha, beta, gamma, delta, and Omicron with possible symptoms, viz. runny nose, fever, sore throat, diarrhea, and difficulty breathing accompanied by severe body pain. The recommended strategy would deliver reliable and improved diagnostics against possible threats due to SARS-CoV mutations, including possible pathogens in the future.

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Section: Artificial Intelligence In Biosensor Synthesismentioning

confidence: 99%

Prospects of Using Machine Learning and Diamond Nanosensing for High Sensitivity SARS-CoV-2 Diagnosis

2023

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Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Sun

Jiao

Shi

et al. 2022

Computational and Structural Biotechnology Journal

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Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene

Hadiby,

Ben Ali

2024

J. Bioinform. Comput. Biol.

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In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints. The deep learning model was designed to learn complex patterns and relationships between the pharmacophore features and the corresponding activity/inactivity labels of the molecules. By utilizing this integrated approach, we aimed to enhance the accuracy and efficiency of activity prediction. To validate the effectiveness of our approach, we conducted experiments using a dataset of known molecules with BRCA1 gene activity/inactivity from diverse sources. Our results demonstrated promising predictive performance, indicating the successful integration of pharmacophore modeling and deep learning. Furthermore, we utilized the trained model to predict the activity/inactivity of unknown molecules extracted from the ChEMBL database. The predictions obtained from the ChEMBL database were assessed and compared against experimentally determined values to evaluate the reliability and generalizability of our model. Overall, our proposed approach showcased significant potential in accurately predicting the activity/inactivity of molecules with the BRCA1 gene, thus enabling the identification of potential candidates for further investigation in drug discovery and development processes.

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Deep learning application detecting SARS-CoV-2 key enzymes inhibitors

Cited by 3 publications

References 63 publications

Prospects of Using Machine Learning and Diamond Nanosensing for High Sensitivity SARS-CoV-2 Diagnosis

Prospects of Using Machine Learning and Diamond Nanosensing for High Sensitivity SARS-CoV-2 Diagnosis

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene

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