Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19

Zhe Li; Chengkun Wu; Yishui Li; Runduo Liu; Kai Lu; Ruibo Wang; Jie Liu; Chunye Gong; Canqun Yang; Xin Wang; Chang Guo Zhan; Hai Bin Luo

doi:10.1177/10943420221117797

Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19

Zhe Li, Chengkun Wu, Yishui Li, Runduo Liu, Kai Lu, Ruibo Wang, Jie Liu, Chunye Gong, Canqun Yang, Xin Wang, Chang Guo Zhan, Hai Bin Luo

Research output: Contribution to journal › Article › peer-review

Abstract

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 M^pro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards M^pro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

Original language	English
Pages (from-to)	45-57
Number of pages	13
Journal	International Journal of High Performance Computing Applications
Volume	37
Issue number	1
DOIs	https://doi.org/10.1177/10943420221117797
State	Published - Jan 2023

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We cordially acknowledge National Key R&D Program of China (2017YFB0202600), National Natural Science Foundation of China (81903542, 21877134, 22077143, and U1811462), Fundamental Research Funds for Hainan University (KYQD (ZR)-21,031), Science Foundation of Guangzhou City (202102021151, 201904020023), Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme (2016), the National Science Foundation (NSF, grant CHE-1111761), the Taishan Scholars Program (tsqn201909170), the Innovative Leader of Qingdao Program (19-3-2–26-zhc), the special scientific research fund for COVID-19 from the Pilot National Laboratory for Marine Science and Technology (QNLM202001), and open fund from the State Key Laboratory of High Performance Computing (201901–11).

Publisher Copyright:
© The Author(s) 2022.

Keywords

SARS-CoV-2
Supercomputing
absolute binding free energy
free energy perturbation
virtual screening

ASJC Scopus subject areas

Theoretical Computer Science
Software
Hardware and Architecture

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10.1177/10943420221117797

Cite this

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title = "Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19",

abstract = "As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.",

keywords = "SARS-CoV-2, Supercomputing, absolute binding free energy, free energy perturbation, virtual screening",

author = "Zhe Li and Chengkun Wu and Yishui Li and Runduo Liu and Kai Lu and Ruibo Wang and Jie Liu and Chunye Gong and Canqun Yang and Xin Wang and Zhan, {Chang Guo} and Luo, {Hai Bin}",

note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We cordially acknowledge National Key R&D Program of China (2017YFB0202600), National Natural Science Foundation of China (81903542, 21877134, 22077143, and U1811462), Fundamental Research Funds for Hainan University (KYQD (ZR)-21,031), Science Foundation of Guangzhou City (202102021151, 201904020023), Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme (2016), the National Science Foundation (NSF, grant CHE-1111761), the Taishan Scholars Program (tsqn201909170), the Innovative Leader of Qingdao Program (19-3-2–26-zhc), the special scientific research fund for COVID-19 from the Pilot National Laboratory for Marine Science and Technology (QNLM202001), and open fund from the State Key Laboratory of High Performance Computing (201901–11). Publisher Copyright: {\textcopyright} The Author(s) 2022.",

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AU - Wu, Chengkun

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AU - Liu, Runduo

AU - Lu, Kai

AU - Wang, Ruibo

AU - Liu, Jie

AU - Gong, Chunye

AU - Yang, Canqun

AU - Wang, Xin

AU - Zhan, Chang Guo

AU - Luo, Hai Bin

N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We cordially acknowledge National Key R&D Program of China (2017YFB0202600), National Natural Science Foundation of China (81903542, 21877134, 22077143, and U1811462), Fundamental Research Funds for Hainan University (KYQD (ZR)-21,031), Science Foundation of Guangzhou City (202102021151, 201904020023), Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme (2016), the National Science Foundation (NSF, grant CHE-1111761), the Taishan Scholars Program (tsqn201909170), the Innovative Leader of Qingdao Program (19-3-2–26-zhc), the special scientific research fund for COVID-19 from the Pilot National Laboratory for Marine Science and Technology (QNLM202001), and open fund from the State Key Laboratory of High Performance Computing (201901–11). Publisher Copyright: © The Author(s) 2022.

PY - 2023/1

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N2 - As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

AB - As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

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Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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