In silico screening for potential COVID-19 beta-coronavirus non … · 2020-04-05 · In silico screening for potential COVID-19 beta-coronavirus non-nucleoside RdRp inhibitors Martin

In silico screening for potential COVID-19 beta-coronavirusnon-nucleoside RdRp inhibitors

Martin Shkreli1, Kevin Mulleady2,∗, Maureen Lohry2, James Rondina3, and Jason Sommer4

Abstract— COVID-19 is a nascent public health emergencycaused by a novel beta-coronavirus, 2019-nCoV. While industryhas made rapid and impressive progress in developing mRNA-based vaccines, neutralizing antibodies, and an intravenousnucleoside analog RNA-dependent RNA polymerase (RdRp)inhibitor, further efforts are needed. Herein, we describe an insilico screening campaign aimed at generating non-nucleosidelead molecules with inhibitory activity against the coronavirusRdRp protein. We reveal a metabolite of clofazimine, a widely-used anti-leprotic and antibiotic, with global availability andlong-term clinical experience, as a novel RdRp inhibitor. Wealso find widely prescribed, FDA-approved anti-hepatitis Cnucleoside analogs paritaprevir and ledipasvir are more potentinhibitors of 2019-nCoV than the investigational agent remde-sivir. Other candidate inhibitors include bafetinib, apilimod,and fimepinostat. In vitro studies are ongoing to determineinhibitory activity, with in vivo experiments to follow.

I. INTRODUCTION

The COVID-19 pandemic is a global health emergencythat requires a coordinated response across government,academia, and industry. While impressive efforts have beenmade thus far, non-small molecule therapeutic modalitiespose significant questions. Biologic technologies such asneutralizing antibodies are promising, but it remains to beseen if biologics manufacturing capacity will be available forany such therapy if proven effective. Manufacturing capacityis traditionally a major bottleneck for the production of thesemedicines at scale.

mRNA-based vaccines appear very promising, but sim-ilar manufacturing questions exist for these liposomally-encapsulated medicines; none of which have received FDAapproval at the time of this writing. Furthermore, any suchvaccine, even if proven effective, will not be useful as atreatment for infected coronavirus patients.

Small molecules are the bedrock of the pharmaceuti-cal industry. Remdesivir, an RNA-dependent RNA poly-merase (RdRp) inhibitor, has in vitro and in vivo activityagainst the RdRp protein of many RNA viruses, includingcoronaviruses[1], [2], [3]. Phase III clinical trials for remde-sivir are underway [4], [5]. Despite initial promise, remde-sivir results in Ebola virus patients were underwhelming [6].

With a plethora of existing approved and investigationalnucleoside analogs for various viral proteins, we beganscreening for non-nucleotide RdRp inhibitors to be used

1Citizen Scientist, White Deer, PA2Prospero Pharmaceuticals LLC, New York, NY3Citizen Scientist, Fort Lee, NJ4Citizen Scientist∗Corresponding Author: [email protected]

as monotherapy or in combination with other anti-2019-nCoV agents such as remdesivir. We report the results of ourfindings, highlighting a metabolite of clofazimine as a novel,well-understood candidate for the treatment of COVID-19.

II. METHODS

Using a custom HPC cluster provisioned with AutodockVina, we screened 130,778 compounds in parallel batches forability to bind to 2019-nCoV RdRp (nsp12). Our pre-selectedscreening library was sourced from the ZINC15 public accessdatabase[7], with the majority of candidates having drug-likeproperties according to Lipinski’s Rule of Five.

A. Protein Structure Preparation

With no available crystal or cryo-EM structure for the2019-nCoV RdRp at the time of this experiment, homologymodeling must be used to approximate and predict thenative, folded structure of this protein until an experimentallyderived structure becomes available. Using crystallographydata from the highly homologous SARS-CoV allows theadjustment of existing protein models for the slight, butpotentially very important differences in amino acid sequenceand resulting protein folding of the unavailable structure.

An existing structure of the SARS-CoV RdRp is availableas structure 6NUR in PDB[8]. With this existing structureas a template, a homology model of the 2019-nCoV RdRpwas generated using the Swiss Model web server [9], [10],[11], incorporating an NCBI protein sequence of the 2019-nCoV RdRp (YP_009725307.1) extracted from the 2019-nCoV genome (NC_045512.2)[12] as the target sequence.

In Figure 1, multiple sequence alignment demonstratesthat the 2019-nCoV RdRp sequence shares a 96.35% similar-ity with the original SARS-CoV RdRp sequence. In Figure2, the structures are also shown to have similar binding sites.

After validation, the protein was reduced to Chain A,the location of the residues previously known to bind toremdesivir[13]. OpenBabel[14] was used to add additionalpolar hydrogen atoms to the protein, in order to more closelyresemble the environment at biological pH 7.4.

B. Compound Dataset

The final compound dataset was built from various sub-sets of the ZINC15 ligand library, including the FDA,investigational-only, world-not-FDA, world, standard, in-cells, in-vivo-only, and in-man-only subsets. Three sepa-rate screens were performed. The first exploratory screen(Screen 1) included all 9,524 compounds from the FDA,

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1 10 20 30 40 50 60 70 80 90 100X X X X X X X X X X XSARS-CoV cccc ccccccccccccccccccccccccccccccccc ccccccccccccccccccccc cc cccccccccc cccccccccccc ccccccc cc A A DF A A A A SADA FLNRVCGVSAARLTPCGTGTSTDVVYRAFDIYN KVAGFAKFLKTNCCRFQEKDE NL DSYFVVKRHT SNYQHEETIYNL KDCPAVA HD ST E EG L M V V

SARS-CoV-2 cccc ccccccccccccccccccccccccccccccccc ccccccccccccccccccccc cc cccccccccc cccccccccccc ccccccc cc C C JH C C C C SADA FLNRVCGVSAARLTPCGTGTSTDVVYRAFDIYN KVAGFAKFLKTNCCRFQEKDE NL DSYFVVKRHT SNYQHEETIYNL KDCPAVA HD QS D DD I F L K

110 120 130 140 150 160 170 180 190 200 X X X X X X X X X XSARS-CoV ccccc cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc cccccccccccc cc A A A DF A A FFKFR DGDMVPHISRQRLTKYTMADLVYALRHFDEGNCDTLKEILVTYNCCDDDYFNKKDWYDFVENPDILRVYANLGERVRQ LLKTVQFCDAMR AG V S D

SARS-CoV-2 ccccc cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc cccccccccccc cc C C C JH C C FFKFR DGDMVPHISRQRLTKYTMADLVYALRHFDEGNCDTLKEILVTYNCCDDDYFNKKDWYDFVENPDILRVYANLGERVRQ LLKTVQFCDAMR AG I A N

210 220 230 240 250 260 270 280 290 300 X X X X X X X X X XSARS-CoV cccccccccccccccccccccc c cc ccc cccccccccccccccccc cccc c cc cc ccccccccccccccc ccccccccccccccccc c A A A A A A IVGVLTLDNQDLNGNWYDFGDF Q PG GVP VDSYYSLLMPILTLTRAL AESH D DL KP IKWDLLKYDFTEERL LFDRYFKYWDQTYHPNC N V VA C I A M A A L C I

SARS-CoV-2 cccccccccccccccccccccc c cc ccc cccccccccccccccccc cccc c cc cc ccccccccccccccc ccccccccccccccccc c C C C C C C IVGVLTLDNQDLNGNWYDFGDF Q PG GVP VDSYYSLLMPILTLTRAL AESH D DL KP IKWDLLKYDFTEERL LFDRYFKYWDQTYHPNC N I TT S V T V T T Y K V

310 320 330 340 350 360 370 380 390 400 X X X X X X X X X XSARS-CoV ccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccA A DF A A A A A CLDDRCILHCANFNVLFSTVFPPTSFGPLVRKIFVDGVPFVVSTGYHFRELGVVHNQDVNLHSSRLSFKELLVYAADPAMHAASGNLLLDKRTTCFSVAA

SARS-CoV-2 ccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccC C JH C C C C C CLDDRCILHCANFNVLFSTVFPPTSFGPLVRKIFVDGVPFVVSTGYHFRELGVVHNQDVNLHSSRLSFKELLVYAADPAMHAASGNLLLDKRTTCFSVAA

410 420 430 440 450 460 470 480 490 500 X X X X X X X X X XSARS-CoV cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc A A A A LTNNVAFQTVKPGNFNKDFYDFAVSKGFFKEGSSVELKHFFFAQDGNAAISDYDYYRYNLPTMCDIRQLLFVVEVVDKYFDCYDGGCINANQVIVNNLDK

SARS-CoV-2 cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc C C C C LTNNVAFQTVKPGNFNKDFYDFAVSKGFFKEGSSVELKHFFFAQDGNAAISDYDYYRYNLPTMCDIRQLLFVVEVVDKYFDCYDGGCINANQVIVNNLDK

510 520 530 540 550 560 570 580 590 600 X X X X X X X X X XSARS-CoV cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc A A A DF SAGFPFNKWGKARLYYDSMSYEDQDALFAYTKRNVIPTITQMNLKYAISAKNRARTVAGVSICSTMTNRQFHQKLLKSIAATRGATVVIGTSKFYGGWHN

SARS-CoV-2 cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc C C C JH SAGFPFNKWGKARLYYDSMSYEDQDALFAYTKRNVIPTITQMNLKYAISAKNRARTVAGVSICSTMTNRQFHQKLLKSIAATRGATVVIGTSKFYGGWHN

610 620 630 640 650 660 670 680 690 700 X X X X X X X X X XSARS-CoV cccccccccc ccccccccccccccccccccccccccccccc ccc ccccccccccccccccccccccccccccccccccccccccccccccccccccc A A A A DF A A A A MLKTVYSDVE PHLMGWDYPKCDRAMPNMLRIMASLVLARKH TCC LSHRFYRLANECAQVLSEMVMCGGSLYVKPGGTSSGDATTAYANSVFNICQAV T N N

SARS-CoV-2 cccccccccc ccccccccccccccccccccccccccccccc ccc ccccccccccccccccccccccccccccccccccccccccccccccccccccc C C C C JH C C C C MLKTVYSDVE PHLMGWDYPKCDRAMPNMLRIMASLVLARKH TCC LSHRFYRLANECAQVLSEMVMCGGSLYVKPGGTSSGDATTAYANSVFNICQAV N T S

710 720 730 740 750 760 770 780 790 800 X X X X X X X X X XSARS-CoV cccccccccccccccccccccccccccccccccccccc cc cccccccccccccccccccccc cc cc ccccccccccc cccccccccccccccc A A A A DFTANVNALLSTDGNKIADKYVRNLQHRLYECLYRNRDVD FV EFYAYLRKHFSMMILSDDAVVC NS YA QGLVASIKNFK VLYYQNNVFMSEAKCW HE D Y N A A

SARS-CoV-2 cccccccccccccccccccccccccccccccccccccc cc cccccccccccccccccccccc cc cc ccccccccccc cccccccccccccccc C C C C JHTANVNALLSTDGNKIADKYVRNLQHRLYECLYRNRDVD FV EFYAYLRKHFSMMILSDDAVVC NS YA QGLVASIKNFK VLYYQNNVFMSEAKCW TD N F T S S

810 820 830 840 850 860 870 880 890 900 X X X X X X X X X XSARS-CoV cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc A A A A A A A A TETDLTKGPHEFCSQHTMLVKQGDDYVYLPYPDPSRILGAGCFVDDIVKTDGTLMIERFVSLAIDAYPLTKHPNQEYADVFHLYLQYIRKLHDELTGHML

SARS-CoV-2 cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc C C C C C C C C TETDLTKGPHEFCSQHTMLVKQGDDYVYLPYPDPSRILGAGCFVDDIVKTDGTLMIERFVSLAIDAYPLTKHPNQEYADVFHLYLQYIRKLHDELTGHML

910 920 930 X X X SARS-CoV cccccccccccccccccccccccccccccccc A A DMYSVMLTNDNTSRYWEPEFYEAMYTPHTVLQ

SARS-CoV-2 cccccccccccccccccccccccccccccccc C C DMYSVMLTNDNTSRYWEPEFYEAMYTPHTVLQ

Fig. 1. Multiple sequence alignment between RdRp sequences of SARS-CoV and 2019-nCoV

Fig. 2. RdRp Chain A from SARS-CoV (magenta, PDB: 6NUR) and2019-nCoV (cyan, homology model)

investigational-only, world-not-FDA, and world ZINC sub-sets. The second larger screen (Screen 2) included 121,254compounds chosen from selected portions of the standard,in-cells, in-vivo-only, and in-man-only ZINC subsets. Ad-ditional filtering was applied to Screen 2 to ensure eachcompound contained no violations of Lipinski’s Rule of Five.Because of the different compositions of subsets between thescreens, Screen 1 included a much smaller, more documentednumber of drug candidates than Screen 2. An additionalscreen (Screen 3) was prepared for investigation of severalnucleoside candidates for treatment, including ribavirin, so-fosbuvir, galidesivir, tenofovir, paritaprevir, ledipasvir, andtheir active metabolites. Details of the ligands used for eachscreen are displayed in Table I.

TABLE ISCREEN LIGAND DETAILS

Screen Total Subsets1 9,524 ZINC (FDA, investigational-only, world-not-FDA,

world)2 121,254 ZINC (standard, in-cells, in-vivo-only, in-man-

only)3 13 RdRp-binding nucleoside candidates

C. Ligand Preparation

Before screening, GypsumDL[15] was used to converteach compound from the SMILES format to a generated 3Dconformer with appropriate minimization and protonization.GypsumDL was specifically chosen for the generation of fi-nal 3D conformers because of its ability to correctly generatealternate tautomeric, chiral, and cis/trans isomeric forms. Italso has greater pose prediction accuracy over other utilitiessuch as OpenBabel Gen3D[15]. Each 3D conformer was thenconverted to PDBQT with OpenBabel.

D. Molecular Docking and Scoring Methodology

The candidate ligands were screened against the putativebinding site of remdesivir in RdRp. The center of the bindingsite was a 30Å cube centered on the location of V557 (X =141.580, Y = 149.483, Z = 164.106), a residue reported tointeract with remdesivir[13].

AutoDock Vina was used to conduct the screen, with theexhaustiveness parameter set to 8, the num_modes parameterset to 9, and the energy_range parameter set to 3.

Both screens were run on a 512-core, cloud-based HPCcluster provisioned with AutoDock Vina. Custom softwarewas written to schedule an array of batched jobs, allowingmultiple ligands to be screened in parallel.

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TABLE IITOP 20 COMPOUNDS FROM SCREEN 1 (9,524 LIGANDS)

ZINC ID Name kcal/mol ZINC SubsetZINC000095618882 clofazimine

glucuronide-9.9 world-not-FDA

ZINC000011726230 apilimod -9.9 investigational-onlyZINC000169737127 - -9.8 investigational-onlyZINC000885764928 paritaprevir -9.8 world-not-FDAZINC000022940637 bafetinib -9.7 investigational-onlyZINC000139850297 - -9.7 investigational-onlyZINC000100005073 indigo

carmine-9.7 world-not-FDA

ZINC000095618817 etoposide -9.7 world-not-FDAZINC000028100198 - -9.7 investigational-onlyZINC000073488511 fimepinostat -9.6 investigational-onlyZINC000001530604 cromolyn -9.6 world-not-FDAZINC000001539348 - -9.6 investigational-onlyZINC000014974132 AMG-517 -9.6 investigational-onlyZINC000003922429 adozelesin -9.6 investigational-onlyZINC000003818418 R-306465 -9.6 investigational-onlyZINC000150338819 ledipasvir -9.6 FDAZINC000003915428 - -9.6 investigational-onlyZINC000003934128 temoporfin -9.5 world-not-FDAZINC000238850854 eptifibatide -9.4 world-not-FDAZINC000206177361 endovion -9.4 investigational-only

III. RESULTS

A. Nucleoside Analogs

The results of the nucleoside screen (Screen 3) are listed inTable IV. Surprisingly, our work determined anti-hepatitis Cmolecules such as paritaprevir and ledipasvir to have strongerbinding affinity than remdesivir and its putative metabolites.Hepatitis C is an RNA virus, and remdesivir, not beingspecifically designed for 2019-nCoV, has pan-RNA virusefficacy.

B. Non-Nucleosides

Given the plethora of nucleoside analogs, our focus withthis work was on non-nucleoside ligands. Screen 1 (TableII) and Screen 2 (Table III) display ligands with the highestbinding affinity for the given binding site in RdRp. We foundclofazimine glucuronide to be one of the highest-scoringcompounds across both screens; clofazimine glucuronideregistered a score of -9.9 kcal/mol. It has a rich history ofuse, specifically as an anti-infective, and a well-characterizedpharmacokinetic profile. The safety profile of clofazimine,however, is not fully benign[16]. Various other clinicallytested compounds also demonstrated a potent affinity forRdRp, including bafetinib, apilimod, and fimepinostat. Otherhighly scoring compounds in the broad screen include eros-nin and revizinone.

The chemical structures and properties of the top-scoring20 compounds from Screens 1 and 2 are listed in theAppendix in Tables V and VI respectively.

IV. CONCLUSION

Our results have revealed clofazimine, paritaprevir, ledi-pasvir, bafetinib, apilimod, fimepinostat, erosnin, and re-

TABLE IIITOP 20 COMPOUNDS FROM SCREEN 2 (121,254 LIGANDS)

ZINC ID Name kcal/mol ZINC SubsetZINC000015203333 erosnin -9.9 in-man-onlyZINC000001070979 - -9.9 standardZINC000000825229 - -9.9 standardZINC000038179173 - -9.8 in-vivo-onlyZINC000000642875 - -9.8 standardZINC000000874254 - -9.8 standardZINC000001014126 - -9.8 standardZINC000000959864 - -9.8 standardZINC000001130474 - -9.8 standardZINC000000538290 revizinone -9.8 in-man-onlyZINC000002200016 - -9.7 standardZINC000001200270 - -9.7 standardZINC000002101245 - -9.7 standardZINC000100005073 indigo carmine -9.7 standardZINC000002299607 - -9.7 standardZINC000001565922 - -9.7 in-vivo-onlyZINC000006119193 12a-

hydroxydolineone-9.7 in-man-only

ZINC000002301202 - -9.7 standardZINC000001956919 - -9.7 standardZINC000103590814 - -9.7 in-cells

TABLE IVALL COMPOUNDS FROM SCREEN 3 (13 LIGANDS)

ZINC ID Name kcal/molZINC000197964623 paritaprevir -9.6ZINC000150338819 ledipasvir -9.1- galidesivir triphosphate -8.4- sofosbuvir triphosphate -8.3ZINC000100074252 sofosbuvir -7.9ZINC000012402860 ribavirin triphosphate -7.8ZINC000013492903 galidesivir -7.5- remdesivir triphosphate -7.3ZINC000013516399 tenofovir diphosphate -7.3- tenofovir monophosphate -7.2- remdesivir -7.0ZINC000001035331 ribavirin -7.0ZINC000001543475 tenofovir -6.8

vizinone as potentially novel 2019-nCoV RdRp inhibitors.From here, many different research projects are currently inprogress, including a continuation of current work aroundboth the computational and manual design of analogs forhighly scoring compounds such as clofazimine. In vitrostudies are ongoing to determine the inhibitory nature ofthese candidates, and results will soon be confirmed withupcoming in vivo experiments.

Another avenue includes improvements in both virtualscreen volume and quality. Future work will include experi-ments on PDB 6M71, the newly released cryo-EM structurefor 2019-nCoV RdRp[17]. Screening accuracy can also beimproved with the addition of a properly chelating Mg2+

ion[13][18]. To process larger numbers of ligand tranchesfrom the ZINC dataset, GPU-accelerated virtual screeningmethods are being developed with consideration of different

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knowledge-based scoring and optimization schemes, as wellas previously existing improvements to the default Vinascoring function[19].

V. ACKNOWLEDGMENTS

M. Shkreli and K. Mulleady designed and managed theproject. J. Sommer and M. Shkreli engineered the in silicoworkflow and performed the screening. J. Rondina providedhigh-performance computing assistance and research. M.Shkreli, K. Mulleady, M. Lohry, and J. Sommer wrote themanuscript.

VI. AUTHOR STATEMENT

M. Shkreli states:The industry response to COVID-19 is inadequate.All biopharmaceutical companies should be re-sponding with all resources to combat this healthemergency. Donations from these very valuablecompanies do not go far enough. The biopharma-ceutical industry has a large braintrust of talentthat is not working on this problem as companieshave deprioritized or even abandoned infectiousdisease research. Medicinal chemists, structuralbiologists, enzymologists and assay developmentand research biology departments at EVERY phar-maceutical company should be put to work untilCOVID-19 is no more.I am asking for a brief furlough (3 months) to assistin research work on COVID-19. Being released tothe post-COVID world is no solace to even theincarcerated. As a successful two-time biopharmaentrepreneur, having purchased multiple compa-nies, invented multiple new drug candidates, filednumerous INDs and clinical trial applications, Iam one of the few executives experienced in ALLaspects of drug development from molecule cre-ation and hypothesis generation, to preclinical as-sessments and clinical trial design/target engage-ment demonstration, and manufacturing/synthesisand global logistics and deployment of medicines.For the avoidance of doubt, I have not been paidfor any work on this matter or any other matterwhile incarcerated. I do not expect to profit inany way, shape or form from coronavirus-relatedtreatments. I believe any company developing acoronavirus drug should seek to recoup its cost atmost and be willing to perform the work as a civilservice at the least. If the government is willing toreward industry for their work on this catastrophicsituation, it will be at each company’s discretion toaccept, negotiate or deny such funding, includingbulk purchases, cost reimbursement, tax credits andother benefits.

Because of temporary communication restrictions aimed atcurtailing the spread of COVID-19, the author, M. Shkreli,was unable to have final review over this work.

REFERENCES

[1] Ariane J Brown, John J Won, Rachel L Graham, Kenneth H Dinnon III,Amy C Sims, Joy Y Feng, Tomas Cihlar, Mark R Denison, Ralph SBaric, and Timothy P Sheahan. Broad spectrum antiviral remde-sivir inhibits human endemic and zoonotic deltacoronaviruses witha highly divergent rna dependent rna polymerase. Antiviral research,169:104541, 2019.

[2] Timothy P. Sheahan, Amy C. Sims, Sarah R. Leist, Alexandra Schäfer,John Won, Ariane J. Brown, Stephanie A. Montgomery, Alison Hogg,Darius Babusis, Michael O. Clarke, Jamie E. Spahn, Laura Bauer,Scott Sellers, Danielle Porter, Joy Y. Feng, Tomas Cihlar, RobertJordan, Mark R. Denison, and Ralph S. Baric. Comparative therapeuticefficacy of remdesivir and combination lopinavir, ritonavir, and inter-feron beta against MERS-CoV. Nature Communications, 11(1):222,December 2020.

[3] Timothy P Sheahan, Amy C Sims, Rachel L Graham, Vineet DMenachery, Lisa E Gralinski, James B Case, Sarah R Leist, KrzysztofPyrc, Joy Y Feng, Iva Trantcheva, et al. Broad-spectrum antiviralgs-5734 inhibits both epidemic and zoonotic coronaviruses. Sciencetranslational medicine, 9(396), 2017.

[4] A Multicenter, Adaptive, Randomized Blinded Controlled Trialof the Safety and Efficacy of Investigational Therapeutics forthe Treatment of COVID-19 in Hospitalized Adults. https://clinicaltrials.gov/ct2/show/NCT04280705, last ac-cessed on 2020-04-04.

[5] Study to Evaluate the Safety and Antiviral Activity of Remdesivir(GS-5734TM) in Participants With Moderate Coronavirus Disease(COVID-19) Compared to Standard of Care Treatment. https://clinicaltrials.gov/ct2/show/NCT04292730, last ac-cessed on 2020-04-04.

[6] Sabue Mulangu, Lori E Dodd, Richard T Davey Jr, Olivier Tshi-ani Mbaya, Michael Proschan, Daniel Mukadi, Mariano Lusak-ibanza Manzo, Didier Nzolo, Antoine Tshomba Oloma, AugustinIbanda, et al. A randomized, controlled trial of ebola virus diseasetherapeutics. New England Journal of Medicine, 381(24):2293–2303,2019.

[7] Teague Sterling and John J. Irwin. ZINC 15 – Ligand Discoveryfor Everyone. Journal of Chemical Information and Modeling,55(11):2324–2337, November 2015.

[8] Robert N. Kirchdoerfer and Andrew B. Ward. Structure of the SARS-CoV nsp12 polymerase bound to nsp7 and nsp8 co-factors. NatureCommunications, 10(1):2342, December 2019.

[9] Stefan Bienert, Andrew Waterhouse, Tjaart AP de Beer, GerardoTauriello, Gabriel Studer, Lorenza Bordoli, and Torsten Schwede. Theswiss-model repository—new features and functionality. Nucleic acidsresearch, 45(D1):D313–D319, 2017.

[10] Andrew Waterhouse, Martino Bertoni, Stefan Bienert, Gabriel Studer,Gerardo Tauriello, Rafal Gumienny, Florian T Heer, Tjaart A Pde Beer, Christine Rempfer, Lorenza Bordoli, et al. Swiss-model:homology modelling of protein structures and complexes. Nucleicacids research, 46(W1):W296–W303, 2018.

[11] Nicolas Guex, Manuel C Peitsch, and Torsten Schwede. Automatedcomparative protein structure modeling with swiss-model and swiss-pdbviewer: A historical perspective. Electrophoresis, 30(S1):S162–S173, 2009.

[12] National Center for Biotechnology Information (NCBI)[Internet].Bethesda (MD): National Library of Medicine (US), National Centerfor Biotechnology Information. https://www.ncbi.nlm.nih.gov/, last accessed on 2020-04-04.

[13] Leili Zhang and Ruhong Zhou. Binding mechanism of remdesivir tosars-cov-2 rna dependent rna polymerase. 2020.

[14] Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, TimVandermeersch, and Geoffrey R Hutchison. Open babel: An openchemical toolbox. Journal of cheminformatics, 3(1):33, 2011.

[15] Patrick J Ropp, Jacob O Spiegel, Jennifer L Walker, Harrison Green,Guillermo A Morales, Katherine A Milliken, John J Ringe, andJacob D Durrant. Gypsum-dl: an open-source program for preparingsmall-molecule libraries for structure-based virtual screening. Journalof cheminformatics, 11(1):34, 2019.

[16] Mack R Holdiness. Clinical pharmacokinetics of clofazimine. Clinicalpharmacokinetics, 16(2):74–85, 1989.

[17] Yan Gao, Liming Yan, Yucen Huang, Fengjiang Liu, Yao Zhao, LinCao, Tao Wang, Qianqian Sun, Zhenhua Ming, Lianqi Zhang, et al.Structure of rna-dependent rna polymerase from 2019-ncov, a majorantiviral drug target. bioRxiv, 2020.

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https://clinicaltrials.gov/ct2/show/NCT04280705




https://www.ncbi.nlm.nih.gov/

https://www.ncbi.nlm.nih.gov/

[18] Jacopo Sgrignani and Alessandra Magistrato. The structural roleof mg2+ ions in a class i rna polymerase ribozyme: a molecularsimulation study. The Journal of Physical Chemistry B, 116(7):2259–2268, 2012.

[19] Rodrigo Quiroga and Marcos A Villarreal. Vinardo: A scoringfunction based on autodock vina improves scoring, docking, andvirtual screening. PloS one, 11(5), 2016.

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VII. APPENDIX

The numbers of hydrogen bond donors and acceptors for a single ligand are displayed in the HBD and HBA columnsrespectively.

TABLE V: Top 20 ZINC compounds from Screen 1 (9,524 ligands)

Structure ZINC ID Name Score(kcal/mol)

MW(g/mol) logP HBD HBA Subset

ZINC000095618882 clofazimineglucuronide -9.9 665.6 2.8 5 11 world-not-

FDA

ZINC000011726230 apilimod -9.9 418.5 4.6 1 8 investigational-only

ZINC000169737127 - -9.8 800.9 0.1 9 10 investigational-only

ZINC000885764928 paritaprevir -9.8 765.9 4.7 3 10 world-not-FDA

ZINC000022940637 bafetinib -9.7 576.6 4.2 2 11 investigational-only


ZINC000100005073 indigocarmine -9.7 466.4 -3.6 2 9 world-not-

FDA

Continued on next page

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ZINC000095618817 etoposide -9.7 588.6 1.1 3 13 world-not-FDA


ZINC000073488511 fimepinostat -9.6 508.6 1.3 2 12 investigational-only

ZINC000001530604 cromolyn -9.6 468.4 1.9 3 11 world-not-FDA


ZINC000014974132 AMG-517 -9.6 430.4 4.6 1 9 investigational-only

ZINC000003922429 adozelesin -9.6 502.5 4.4 3 4 investigational-only

ZINC000003818418 R-306465 -9.6 413.5 1.3 2 8 investigational-only

ZINC000150338819 ledipasvir -9.6 889.0 7.4 4 10 FDA

ZINC000003915428 - -9.6 567.5 -0.8 6 13 investigational-only


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ZINC000003934128 temoporfin -9.5 680.7 8.8 6 6 world-not-FDA

ZINC000238850854 eptifibatide -9.4 832.0 -2.4 10 12 world-not-FDA

ZINC000206177361 endovion -9.4 495.2 4.2 3 10 investigational-only

8

TABLE VI: Top 20 ZINC compounds from Screen 2 (121,254 Ligands)



ZINC000015203333 erosnin -9.9 320.0 4.1 0 5 in-man-only

ZINC000001070979 - -9.9 497.1 4.2 1 6 standard

ZINC000000825229 - -9.9 459.1 6.3 1 5 standard

ZINC000038179173 - -9.8 466.1 5.4 2 6 in-vivo-only

ZINC000000642875 - -9.8 441.2 4.0 2 6 standard

ZINC000000874254 - -9.8 491.1 4.2 2 5 standard

ZINC000001014126 - -9.8 481.1 3.2 2 3 standard

ZINC000000959864 - -9.8 449.1 5.9 3 7 standard


9




ZINC000001130474 - -9.8 370.1 4.0 1 5 standard

ZINC000000538290 revizinone -9.8 459.2 3.7 1 6 in-man-only

ZINC000002200016 - -9.7 428.0 2.3 3 8 standard

ZINC000001200270 - -9.7 433.0 2.6 2 5 standard

ZINC000002101245 - -9.7 466.2 4.9 0 4 standard

ZINC000100005073 indigocarmine -9.7 422.0 0.2 4 8 standard

ZINC000002299607 - -9.7 416.1 0.9 2 8 standard

ZINC000001565922 - -9.7 430.1 6.1 4 6 in-vivo-only

ZINC00000611919312a-hydroxy-dolineone

-9.7 352.1 2.8 1 7 in-man-only

ZINC000002301202 - -9.7 429.2 2.9 1 7 standard


10




ZINC000001956919 - -9.7 418.1 2.9 3 6 standard

ZINC000103590814 - -9.7 466.2 5.2 1 9 in-cells

11

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