Machine Learning Models Identify Inhibitors of SARS-CoV-2

Gawriljuk, V.O.; Zin, Phyo Phyo Kyaw; Puhl, Ana C.; Zorn, Kimberley M.; Foil, Daniel H.; Lane, Thomas R.; Hurst, Brett L.; Tavella, Tatyana Almeida; Costa, Fábio Trindade Maranhão; Premkumar, Lakshmanane; Bernatchez, Jean; Godoy, Andre S.; Oliva, Glaucius; Siqueira-Neto, Jair L.; Madrid, Peter B.; Ekins, Sean

doi:10.1021/acs.jcim.1c00683

Cited by 38 publications

(26 citation statements)

References 78 publications

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“…superconducting quantum processors, using an ADRB2 benchmarking dataset as well as a novel dataset containing COVID-19 inhibitors [20] and reporting results in line with the numerical predictions obtained with classical numerical simulations. The proposed approach is not tied to a specific use case and can be applied to screen any digital database of active/inactive compounds against a desired target, using any third-party cheminformatics package.…”

Section: Introductionsupporting

confidence: 56%

“…Following the above procedure and considerations, we also choose to assess the performance of our method by screening a novel dataset containing known active and inactive COVID-19 inhibitors [20]. This dataset is particularly challenging for ML classification tasks due to the fact that more than 30% of the active or inactive molecules do not share a common scaffold or other recurrent structural features, showing how different and diverse the active molecules are, making challenging the classification based on a purely structural basis (i.e.…”

Section: A Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

Mensa¹,

Şahin²,

Tacchino³

et al. 2022

Preprint

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Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier (SVC) algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.

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Section: Introductionsupporting

confidence: 56%

Section: A Datasetmentioning

confidence: 99%

Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

Mensa¹,

Şahin²,

Tacchino³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, PCA was set along the SARS-CoV-2 molecular fingerprint descriptors to show that the SARS-CoV-2 chemical space is well distributed with inactive and active molecules. 720 Moreover, PCA was also applied to study the motions of the protein during the binding of the ligand by Prasad et al 565 In the initial process of the SARS-CoV-2 entering the host cell, TMPRSS2 and Cathepsins B/L activate the S protein and enable SARS-CoV-2 to invade the host cell through two independent pathways. Therefore, seeking a simultaneous target to both entry pathways would be a good idea to block the virus from entering host cells.…”

Section: Methods and Approachesmentioning

confidence: 99%

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

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Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus–host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein–protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

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“…S. Ekins et al, implemented several machine learning methods to develop predictive models from recent in vitro SARS-CoV-2 inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house library of compounds [ 53 ]. From the compounds predicted using a Bayesian machine learning model, lumefantrine, an antimalarial, was selected for testing and showed limited antiviral activity in cells, whereas it was shown to bind ( K d 259 nM) to the spike protein using microscale thermophoresis.…”

Section: Ligand-based Artificial Intelligence Methods For Small Molec...mentioning

confidence: 99%

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

Floresta

Zagni

Gentile

et al. 2022

IJMS

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The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.

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Machine Learning Models Identify Inhibitors of SARS-CoV-2

Cited by 38 publications

References 78 publications

Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

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