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AI for the repurposing of approved or investigational drugs against COVID-19

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Abstract and Figures

We utilized a deep neural network to search for host-target acting antivirals among experimental and approved drugs with potential activity against coronavirus-born diseases. To achieve the goal we searched for gene expression signatures of molecular perturbations most closely resembling the effects of the COPB2 gene knockout since COPB2 is required for replication of a genetically similar virus SARS-CoV. The majority of the top-scoring molecules were already suggested for repurposing as broad-spectrum antivirals. One of the approved drugs from the list, nitazoxanide, has recently demonstrated activity against SARS-CoV-2. We, therefore, urge prompt experimental characteriza- tion of the other predictions and highlight the potential of modern AI/ML technologies for prompt identification of human-trial ready therapeutics against the world’s most urgent medical needs.
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AI for the repurposing of approved or investigational drugs against COVID-19
Konstantin Avchaciov, Olga Burmistrova, and Peter O. Fedichev
GERO PTE. LTD., 60 Paya Lebar Road #05-40B Paya Lebar Square, Singapore 409051
The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coron-
avirus 2 (SARS-CoV-2), has spread globally since 2019 and reached the pandemic level in 2020.
We utilized a deep neural network to search for host-target acting antivirals among experimental
and approved drugs with potential activity against coronavirus-born diseases. To achieve the goal
we searched for gene expression signatures of molecular perturbations most closely resembling the
effects of the COBP2 gene knockout since COBP2 is required for replication of a genetically similar
virus SARS-CoV. The majority of the top-scoring molecules were already suggested for repurposing
as broad-spectrum antivirals. One of the approved drugs from the list, nitazoxanide, has recently
demonstrated activity against SARS-CoV-2. We, therefore, urge prompt experimental characteriza-
tion of the other predictions and highlight the potential of modern AI/ML technologies for prompt
identification of human-trial ready therapeutics against the world’s most urgent medical needs. We
encourage academic and industrial collaborations to validate the results of this research and further
develop the most successful compounds.
On December 30, 2019, according to some sources,
an artificial-intelligence (AI) company called BlueDot
alerted clients to an unusual bump in pneumonia cases in
Wuhan, China. It was nine days before the World Health
Organization (WHO) officially flagged what we have all
come to know as Coronavirus disease 2019 (COVID-19).
This is an infectious disease caused by severe acute res-
piratory syndrome coronavirus 2 (SARS-CoV-2, previ-
ously referred to as 2019-nCoV). The disease has spread
globally since 2019, resulting in the 2019–20 coronavirus
pandemic. While the majority of cases result in mild
symptoms, some progress to pneumonia and multi-organ
failure [1].The death rate per the number of diagnosed
cases is estimated at between 1% and 5% and varies by
confounding health conditions and availability of health-
care resources [2].
The application of AI technologies is not limited to
sophisticated natural language processing for outbreak
monitoring. In just a few recent weeks we saw announce-
ments from InSilico Medicine and DeepMind joining the
global effort against the disease by contributing their
AI/ML pipelines for the research and drug discovery com-
munity. The companies respectively produced and made
public domain a set of putative small molecules inhibitors
of one of the key virus targets, 3C-like Protease [3], and
the structure predictions for six proteins associated with
SARS-CoV-2 using the most up-to-date version of their
AlphaFold system [4].
No matter how quickly the initial hits can be found
with or without AI, it will take long years for any such
novel molecule to progress through treacherous waters of
clinical trials and end up as medicines in doctors’ hands
at the bedside. The much needed immediate relief can
Correspondence and requests for materials should be addressed
to POF (email:
only be secured by repurposing approved or clinical-stage
investigational drugs fast-tracked into clinical trials [5].
According to the report, most of the drugs currently
tested against SARS-CoV-2 are Direct-acting antiviral
agents (DAAs) and are representatives of a class of drugs
targeting viral proteins. The repurposed drugs in the
clinical trials were initially designed against HIV or in-
fluenza. A small minority of experimental treatments are
the Host-targeting antivirals (HTA) acting against host
factors, including inflammatory cytokines.
In this work, we utilized a deep neural network to
mine gene expression signatures for experimental and ap-
proved drugs with potential activity against coronavirus-
born diseases. To achieve this goal, we searched for gene
expression profiles of the molecular perturbations most
closely mimicking the gene expression signature of the
COBP2 gene knockdown. The gene is required for repli-
cation of SARS-CoV [6], which is closely genetically re-
lated to SARS-Cov-2 with 79% identity [7].
We speculated that the same gene may be necessary
for the replication of the genetically similar SARS-CoV-
2. A large number of the top-scoring molecules were al-
ready suggested for repurposing as broad-spectrum an-
tivirals based on the drug’s performance in experimental
models of viral diseases. One of the approved drugs, ni-
tazoxanide, has recently demonstrated activity against
SARS-CoV-2 [8]. According to the available literature,
a few more drug compounds from our list have been al-
ready tested and found active in models involving related
viruses in vitro, such as MERS-CoV or SARS-CoV. We,
therefore, call for characterization of the remaining top-
ranked compounds against SARS-CoV-2. All the drugs
selected in our study have been already approved for clin-
ical trials. Hence any experimental confirmation of the
activities predicted here may build the rationale for a
trial involving human subjects and, with some effort and
luck, translated into a cost- and time- effective solution
to the unfolding medical emergency.
Deep neural network for batch removal in LINCS
L1000 dataset
Deep learning with its capabilities to approximate gen-
eral non-linear dependencies of input data was recently
demonstrated as a powerful tool for gene expression data
analysis and batch removal. We trained the deep neu-
ral network (DNN) with the architecture similar to that
described in [9]. To enable identification of biologically
similar molecular and genetic perturbations, we gener-
ated compressed 20-dimensional representations (embed-
dings) of the 978-dimensional differential gene expression
signatures (LINCS L1000 data processing at Level 4 [10].
The DNN was constructed of the three blocks:
DenseNet block, Embeddings, and Perturbagen classifier,
see Fig. 1a. The DenseNet was implemented as suggested
in [9] with 32 hidden layers and a growth rate of 48. The
Embeddings layer is a dense layer with 2514 Input size
and 20 output size. The perturbagen classifier was used
to predict the perturbagen class (pert_iname label in the
L1000 dataset) from the embedding vector using addi-
tive margin Softmax (AM-Softmax) [11] with the margin
value was set to 0.2as in [9].
The training data included profiles from LINCS
PHASE I (GEO accession number GSE92742) and
PHASE II (GEO accession number GSE70138). We re-
moved samples related to cell lines in which less than
5000 profiles were measured. The preprocessed dataset
involving 1467244 gene expression profiles corresponding
to 27870 unique molecular perturbation classes was split
into the training and test datasets at 80/20 ratio. The
test dataset did not include gene expression signatures
from the training dataset.
To validate the performance of the DNN we used the
same metrics as proposed in [9]. The best model achieved
the area under the receiver operating characteristic curve
(ROC AUC) recovery of the molecular, 0.871, and ge-
netic, 0.764, perturbations, respectively. Both numbers
were significantly better than 0.656 and 0.616 for the
same molecular perturbation classes in the raw LINCS
L1000 data. The model was able to pull structurally
similar compounds (with Tanimoto similarity score bet-
ter than 0.85) with ROC AUC of 0.745.
To obtain the compressed representation of each molec-
ular perturbation class we computed consensus profiles
for each dose and time by averaging over all the available
repeats and all cell lines. For the analysis, we used the
measurements of the effects of small molecules produced
at 10µM concentration at the time point of 24h(and 96h
for the genetic perturbations).
Search for antivirals against SARS-CoV-2
A set of host factors relevant for SARS-coronavirus
(SARS-CoV) replication was identified by means of a
siRNA library screen targeting the human kinome in [6].
The depletion of the β2subunit of the coatomer protein
complex (COPB2) in the followup experiment produced
the strongest antiviral effect, reduced SARS-CoV protein
expression, and produced a >2-log reduction in virus
We assumed that the interactions between the virus
and host may be shared by the genetically similar viruses
and proceeded with the search for small molecules capa-
ble to recreate the effect of the COPB2 genetic knock-
out. Fortunately, LINCS L1000 database provides gene
expression signatures of a series of shRNA knockouts of
COPB2. We pooled the consensus signature from multi-
ple shRNA experiments and searched for molecular per-
turbations most similar to the knockout. The similarity
measure, the CoV similarity score, is the distance be-
tween the molecular perturbation and COPB2 knockout
signatures in the latent variables space (see Fig. 1b for
the distribution of the computed similarity scores).
The results of the calculations are summarized in Table
1 and divided into categories reflecting their development
status (including approved, and experimental drugs) ac-
cording to the DrugBank database. For each of the drugs,
we provided the Mechanism of Action (MoA) and the
development status (if applicable). We also performed a
literature search for evidence of antiviral activity in ex-
periments involving coronaviruses (the “direct evidence”
column) and other viral infections (“indirect evidence”
Of note, the top-scored molecular perturbation was
Brefeldin A, which is a natural lactone antiviral com-
pound produced by the fungus Penicillium brefeldianum.
This is a good sanity check for the computational
pipeline since according to [12], Brefeldin A treatment
of SARS-CoV-infected cells significantly reduced replica-
tion as well as the accumulation of virus-induced mem-
brane structures (see [6] for mechanistic details relating
Brefeldin A activity and COPB2 knockout).
Direct-acting antiviral agents (DAAs) have been very
successful against viral infections in medical practice.
However, DAAs suffer from several inherent limitations,
including narrow-spectrum antiviral profiles and liability
to drug resistance. In comparison, host targeting an-
tivirals (HTAs) target host proteins that are probably
broadly required for various viral infections. This is why
HTAs are not only perceived but also demonstrated to
exhibit broad-spectrum antiviral activities. In addition,
host proteins are not under the genetic control of the viral
genome, and hence HTAs possess a much higher genetic
barrier to drug resistance as compared with DAAs (see,
e.g., [19] for the most recent review).
In this work, we focused on repurposing of approved
or clinical-stage investigational drugs against COVID-19
and other coronavirus-borne diseases. We turned to the
FIG. 1: a) Deep neural network (DNN) architecture employed for batch-removal and the construction of the
compressed gene expression representation; b) the distributions of the CoV score (similarity to the gene-expression
signature of COPB2 knockdown) for all, all molecular perturbations, and experimental drugs, respectively.
Broad Institute LINCS L1000 database of biological ef-
fects (gene expression signatures) of molecular and ge-
netic perturbations. The resource is commonly used for
MoA studies and drug repurposing. Unfortunately, most
of gene expression variance in transcriptomes is typically
associated with non-biological factors (commonly known
as batch effects) and hence may be of limited use without
sophisticated batch removal procedures [20].
We followed and improved on approach from [9] and
reduced batch effects with the help of a deep neural net-
work. We used the compressed low-dimensional rep-
resentations of the gene expression data from LINCS
L1000 dataset and searched for gene expression profiles
of the molecular perturbations most closely mimicking
the gene expression signature of the COBP2 gene knock-
down. The gene was identified in a full-kinome iRNA
screen and was confirmed as being required for the ge-
netically similar SARS-CoV replication in vitro [6].
Based on the computed similarity to COBP2 knockout,
we generated two lists of predicted antivirals separately
among the approved and investigational drugs. Both ta-
bles were significantly enriched by HTAs with broad ac-
tivity against viruses ranging from coronaviruses (MERS
and SARS) to influenza and HCV.
Notably, the in vitro efficacy (EC50) of the investiga-
tional drugs in models of viral diseases are most of the
time better than that of the approved drugs. This cir-
cumstance may reflect medicinal chemistry advances over
the years as we shift our attention from the “old” and al-
ready approved to the “newer” investigational drugs.
Among the approved drugs, niclosamide and nitazox-
anide are known for broad-spectrum antiviral action and
exhibited low-micromolar range activities in vitro mod-
els of coronavirus diseases. Niclosamide is approved for
the treatment of tapeworm infections and is listed in the
WHO essential medicines. Nitazoxanide, a commercial
antiprotozoal agent with an antiviral potential against
a broad range of viruses including human and animal
coronaviruses. Currently, the drug is listed in multiple
clinical trials against viral diseases. In the most recent
work, nitazoxanide inhibited the SARS-CoV-2, the virus
behind the COVID-19, at a low-micromolar concentra-
tion (EC50 = 2.12µM ;CC50 >35.53µM ; the selectivity
index (SI)>16.76) [8].
Although there is no data on the antiviral activity
of ixazomib, a proteasome inhibitor, there is evidence
that this class of compounds possesses a broad-spectrum
antiviral activity. The clinically approved proteasome
inhibitor bortezomib (PS-341) significantly reduces in-
fluenza A virus and vesicular stomatitis virus propaga-
tion at EC50 = 50 100nM , while exhibiting some cy-
totoxicity [21]. It is hypothesized that bortezomib may
act as an antiviral agent via induction of the type I IFN
response. Authors of [22] report an EC50 of influenza
virus infection by bortezomib of 198nM in human non-
malignant RPE cells (CC50 of bortezomib for these cells
exceeds 10µM). Bortezomib also inhibits herpes simplex
virus (HSV) infection (EC50 = 4 50nM for different
strains) since proteasomal degradation activity is criti-
cal for early steps of HSV infection [23]. Proteasome
TABLE I: The top-scoring drugs according to the predicted antiviral effect.
Molecule CoV-Score MoA Status Direct evidence Indirect evidence
Brefeldin A Arf inhibitor N/A EC50 = 21.4µM (SARS-
CoV) [12] Herpes simplex, Newcas-
tle disease, papillomavirus
and polyomavirus [13]
Niclosamide 0.57 N/A Approved for the treat-
ment of tapeworm infec-
EC50 = 3.12µM (SARS-
CoV) [14] Rhinoviruses (HRV),
influenza virus, Chikun-
gunya virus, Zika virus
Nitazoxanide 0.35 N/A Approved antiprotozoan,
currently is in clinical tri-
als for influenza (Phase
3), rotavirus or norovirus
(Phase 2), Hepatitis B and
EC50 = 2µM
(MERS-CoV) [16],
EC50 = 2.12µM (SARS-
CoV-2) [8]
Influenza EC50 =
0.21.5µM, syncy-
tial virus, parainfluenza,
rotavirus, norovirus,
hepatitis B, hepatitis
C, dengue, yellow fever,
Japanese encephalitis
virus and HIV [17]
Afatinib 0.34 EGFR Approved for the treat-
ment of non-small cell lung
Drugs with the same MoA
were effective in in vivo
models of SARS-Cov
Ixazomib 0.33 Proteasome
inhibitor Approved for the treat-
ment of multiple myeloma Another inhibitor with
the same MoA suppressed
replication of SARS-Cov
at EC50 <1µM
Another inhibitor with
the same MoA suppressed
replication of influenza A
virus at EC 50 <1µM
5more drugs 0.29 0.34 Known MoA 2approved, 3withdrawn N/A Some active against Poly-
Reserpine 0.29 Vesicular
Natural compound. Used
as an antihypertensive (in
combination). Was used as
an antidepressant and to
treat dyskinesia in Hunt-
ington’s disease
EC50 = 3.4µM (SARS-
CoV) [18] N/A
Obatoclax 0.33 Mcl-1 (Bcl-
2 family)
Phase 2 clinical trials
for leukemia, lymphoma,
myelofibrosis, and masto-
N/A Was effective in vitro
against Influenza
A and Zika viruses
(EC50 <0.1µM )
0.23 Hsp90 in-
hibitor Phase 2 clinical trials, on-
cology N/A Was effective in vitro
against Influenza A and
Measles (EC50 <0.1µM )
7more drugs 0.20 0.38 Known MoA Phase 1/2 N/A 2some active against In-
fluenza and Dengue
inhibitor MG-132 inhibits the replication of hepatitis E
and C viruses [24], cytomegalovirus [25].
Inhibition of the proteasome in sub-µM concentra-
tions by different chemical compounds, such as MG-
132 or bortezomib appeared to not only impair entry
but also RNA synthesis and subsequent protein expres-
sion of different CoVs, including mouse hepatitis virus
(MHV), feline infectious peritonitis virus, and, most no-
tably, SARS-CoV [26]. Treatment of mice with SARS-
like pneumonitis induced by murine hepatitis virus strain
1 (MHV-1) with the proteasome inhibitor PDTC (5
mg/kg daily s.c.), MG-132 (0.5mg/kg daily s.c.), or PS-
341 (or bortezomib, 0.25 mg/kg daily s.c.) led to 40%
survival (p < 0.01), with a concomitant improvement of
lung histology, reduced pulmonary viral replication, de-
creased pulmonary STAT phosphorylation, and reduced
pulmonary inflammatory cytokine expression [27].
Reserpine is another predicted “hit” and a natural com-
pound used as an antihypertensive (in combination with
other drugs). Previously, it was identified as a second
most active (EC50 = 3.4µM ) compound against SARS-
CoV in a screening of 10,000 compounds [18].
One of the investigational compounds in our list is the
Hsp90 inhibitor NVP-AUY922. This should not be sur-
prising, since some viruses particularly depend on the
cellular chaperoning apparatus. Accordingly, another
HSP90 inhibitor, 17-DMAG, reduced the endpoint foot
and mouth disease virus titer by more than 10-fold at
0.163µM concentration when applied prior to the infec-
tion [28]. Another HSP90 inhibitor geldanamycin ex-
hibited broad-spectrum activity against viruses includ-
ing SARS-CoV with EC50 ranging between 0.5and 4µM
(HIV-1 and SARS-CoV exhibited the highest sensitivity
to geldanamycin in [29]).
Blocking EGFR receptor kinase activity by approved
inhibitors broadly impaired infection by all major HCV
genotypes and viral escape variants in cell culture and
in a human liver chimeric mouse model in vivo [30].
In EGFR(DSK5) mice with constitutively active EGFR,
SARS-CoV infection causes enhanced lung disease [31,
32]. In a separate work involving Respiratory syncy-
tial virus (RSV) challenge model, EGFR activation sup-
presses IFN regulatory factor (IRF 1)-induced IFN-λpro-
duction and thus contributed to the viral infection. On
the contrary, EGFR inhibition during viral infection aug-
mented IRF1, IFN-λ, and decreased RSV titers [33].
Notably, many survivors of SARS-CoV and apprently
SARS-Cov-2 infections develop pulmonary fibrosis (PF),
with a higher prevalence in older patients. In mouse mod-
els of SARS-CoV pathogenesis, the wound repair path-
way, controlled by the epidermal growth factor receptor
(EGFR), is critical to recovery from SARS-CoV-induced
tissue damage.
Age, as well as pre-existing chronic diseases, are risk
factors for COVID-19 mortality. According to the pro-
portional hazards model from [34], the mortality rate
doubling time is approximately 10 years (HR = 1.10) and
is consistent with mortality rate doubling time from the
Gompertz mortality law. It is therefore intriguing to un-
derstand if there are common mechanisms through which
the aging and the prevalence of age-related diseases may
contribute to adverse outcomes.
One of the most interesting examples from our list in
relation to aging is obatoclax, which is reported to stop
influenza virus replication at a very decent 10nM con-
centration. Obatoclax is the discontinued Bcl-2 inhibitor
similar to navitoclax. Both compounds have been in-
vestigated as a senolytics [35, 36], i.e. belong to a class
of drugs reducing the number of so-called senescent cells
(SCs) [37]. The number of SCs increase with age along
with the level of inflammatory cytokines such as IL-6 as
the part of the Senescence Associated Secretory Pheno-
type (SASP). SCs are implicated in the pathogenesis of
multiple age-related diseases, including Idiopathic Pul-
monary Fibrosis. IL-6 is the target of Tosilizumab and is
now tested in clinical trials against COVID-19 [5].Since
COVID-19 mortality is reported to be associated with
cytokine storm and fibrosis (IPF), it would be interest-
ing to see if senolytic drugs (including Hsp90 inhibitors
with senolytic properties [38]) may provide benefits be-
yond reducing the virus replication rate (most probably
by stimulating apoptosis).
In conclusion, we would like to assert that the pre-
sented work does not include any experimental validation
of the predicted host-targeting antivirals. Nevertheless,
the over-representation of the known with broad antivi-
ral activities among our top-scoring compounds list is
very encouraging. We believe that the calculations pre-
sented here highlight the capabilities of modern AI/ML
pipelines for tackling the most challenging medical prob-
lems almost in real-time. The repurposing of existing
drugs from this work could lead to meaningful solutions
to the COVID-2019 pandemic and other more mundane
diseases further down the road.
We encourage academic and industrial collaborations
to validate the results of this research and further develop
the most successful compounds against COVID-19. The
regulatory and legal status of several identified drug can-
didates allow starting immediate clinical trials.
KA, OB and POF are employees of Gero. PF is a
shareholder of Gero. The company develops holds I.P.
covering use of the drugs mentioned in this draft against
a broad range of viral diseases
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... To this end, recently, many studies are being conducted to determine the effective drugs against the novel coronavirus, SARS-CoV-2, each of which has proposed some older drugs as effective drugs using different AI models, some of which are currently undergoing clinical trials [210,[212][213][214][215][216]. For instance, in an effort, Benevolent AI (London, UK) introduced baricitinib on 4 February 2020, as the potential treatment for COVID-19 by using AI methods [217,218]. ...
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Novel COVID-19 is a public health emergency that poses a serious threat to people worldwide. Given the virus spreading so quickly, novel antiviral medications are desperately needed. Repurposing existing drugs is the first strategy. Anti-parasitic drugs were among the first to be considered as a potential treatment option for this disease. Even though many papers have discussed the efficacy of various anti-parasitic drugs in treating COVID-19 separately, so far, no single study comprehensively discussed these drugs. This study reviews some anti-parasitic recommended drugs to treat COVID-19, in terms of function and in vitro as well as clinical results. Finally, we briefly review the advanced techniques, such as artificial intelligence, that have been used to find effective drugs for the treatment of COVID-19.
... The method is built upon auto-encoders 7 for aberration detection, which leverages the distribution of symptoms to differentiate between diseases. The argument in K. Avchaciov et al. (2020) is that AI systems could predict China's pandemic before surprisingly capturing the planet. The authors investigate how early viral detection can be minimized using AI systems by examining viral outbreaks over the past 20 years. ...
... In developing effective treatments for COVID-19, MLbased repurposing frameworks have used algorithms to identify baricitinib (for rheumatoid arthritis), 38 atazanavir (an anti-human immunodeficiency virus drug) 39 and afatinib (for lung cancer) 40 as potential treatments. Deep learning-based algorithms have helped design six new molecules that could halt SARS-CoV-2 replication 41 and identify ten promising agents from among 4895 drugs. ...
... Due to quick spreading of COVID-19 crisis, previously reported antiviral, antimalarial or antiparasitic drugs (i.e. riboflavin [135], lopinavir [136], oseltamivir [137], lopinavir/ritonavir [138], minocycline [139], tocilizumab [140], ribavirin [141,142], niclosamide [143], corticosteroids [144], and ciclesonide [145]) have been utilized for treatment. The clinical and laboratory trials are challenging to modern medicines [146][147][148] that is still under investigation. ...
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Coronavirus disease 2019 (COVID-19) has become a global pandemic in a short period, where a tragically large number of human lives being lost. It is an infectious pandemic that recently infected more than two hundred countries in the world. Many potential treatments have been introduced, which are considered potent antiviral drugs and commonly reported as herbal or traditional and medicinal treatments. A variety of bioactive metabolites extracts from natural herbal have been reported for coronaviruses with some effective results. Food and Drug Administration (FDA) has approved numerous drugs to be introduced against COVID-19, which commercially available as antiviral drugs and vaccines. In this study, a comprehensive review is discussed on the potential antiviral remedies based on natural and synthetic drugs. This review highlighted the potential remedies of COVID-19 which successfully applied to patients with high cytopathic inhibition potency for cell-to-cell spread and replication of coronavirus.
... Discovering potential drugs: In order to find potential therapeutic drugs for the disease, in [572], a library of 1,670 compounds were processed via deep learning. A DNN was used in [573] to search for host-target acting antivirals among experimental and approved drugs with potential activity against the disease. The algorithm searches for gene expression signatures of molecular purturbations close to the SARS-CoV. ...
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Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
... Another study, which screened the L1000 database for compounds expected to reverse the effect of the knockdown of the coatomer protein complex beta 2 gene (COPB2) (Avchaciov et al., 2020), required for SARS-CoV-1 replication (de Wilde et al., 2015), also suggested reserpine as a potential treatment option for COVID-19. The hypothesis behind that study was that host COPB2 is also required for SARS-CoV-2 replication, since SARS-CoV-1 shares 79% sequence identity with SARS-CoV-2 (Lu et al., 2020). ...
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p>There have been more than 116,000 recorded deaths worldwide to-date caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19), and over 1.8 million individuals are currently infected. Although there are now hundreds of clinical trials for COVID-19, there are currently no effective licensed treatments, while the numbers of infected individuals continue to rise at an exponential rate in many parts of the world. Here, we used a data-driven approach utilizing connectivity mapping and the transcriptional signature of lung carcinoma cells infected with SARS-CoV-2, to search for drugs across the spectrum of medicine that have repurposing potential for treating COVID-19. We also performed chemoinformatic analyses to test whether the identified compounds were predicted to physically interact with the SARS-CoV-2 RNA-dependent RNA polymerase or main protease enzymes. Our study identified commonly prescribed FDA-approved molecules as important candidates for drug repositioning against COVID-19, including flupentixol, reserpine, fluoxetine, trifluoperazine, sunitinib, atorvastatin, raloxifene, butoconazole, and metformin. These drugs should not be taken for treating or preventing COVID-19 without a doctor’s advice, as further research and clinical trials are now needed to elucidate their efficacy for this purpose.</p
... Another study, which screened the L1000 database for compounds expected to reverse the effect of the knockdown of the coatomer protein complex beta 2 gene (COPB2) (Avchaciov et al., 2020), required for SARS-CoV-1 replication (de Wilde et al., 2015), also suggested reserpine as a potential treatment option for COVID-19. The hypothesis behind that study was that host COPB2 is also required for SARS-CoV-2 replication, since SARS-CoV-1 shares 79% sequence identity with SARS-CoV-2 (Lu et al., 2020). ...
The global pandemic of Coronavirus Disease 2019 (COVID-19) has brought the world to a grinding halt. A major cause of concern is the respiratory distress associated mortality attributed to the cytokine storm. Despite myriad rapidly approved clinical trials with repurposed drugs, and time needed to develop a vaccine, accelerated search for repurposed therapeutics is still ongoing. In this review, we present Nitazoxanide a US-FDA approved antiprotozoal drug, as one such promising candidate. Nitazoxanide which is reported to exert broad-spectrum antiviral activity against various viral infections, revealed good in vitro activity against SARS-CoV-2 in cell culture assays, suggesting potential for repurposing in COVID-19. Furthermore, nitazoxanide displays the potential to boost host innate immune responses and thereby tackle the life-threatening cytokine storm. Possibilities of improving lung, as well as multiple organ damage and providing value addition to COVID-19 patients with comorbidities, are other important facets of the drug. The review juxtaposes the role of nitazoxanide in fighting COVID-19 pathogenesis at multiple levels highlighting the great promise the drug exhibits. The in silico data and in vitro efficacy in cell lines confirms the promise of nitazoxanide. Several approved clinical trials world over further substantiate leveraging nitazoxanide for COVID-19 therapy.
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Direct‐acting antiviral agents (DAAs) represent a class of drugs targeting viral proteins and have been demonstrated to be very successful in combating viral infections in clinic. However, DAAs suffer from several inherent limitations, including narrow‐spectrum antiviral profiles and liability to drug resistance, and hence there are still unmet needs in the treatment of viral infections. In comparison, host targeting antivirals (HTAs) target host factors for antiviral treatment. Since host proteins are probably broadly required for various viral infections, HTAs are not only perceived, but also demonstrated to exhibit broad‐spectrum antiviral activities. In addition, host proteins are not under the genetic control of viral genome, and hence HTAs possess much higher genetic barrier to drug resistance as compared with DAAs. In recent years, much progress has been made to the development of HTAs with the approval of chemokine receptor type 5 antagonist maraviroc for human immunodeficiency virus treatment and more in the pipeline for other viral infections. In this review, we summarize various host proteins as antiviral targets from a medicinal chemistry prospective. Challenges and issues associated with HTAs are also discussed.
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Background: A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods: All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings: By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0-58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0-13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation: The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding: Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence¹. This problem is of fundamental importance as the structure of a protein largely determines its function²; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures³. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force⁴ that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction⁵ (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores⁶ of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined⁷.
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Viruses usurp host cell functions to advance their replicative agenda. HSV relies on cellular proteasome activity for successful infection. Proteasome inhibitors, such as MG132, block HSV infection at multiple stages of the infectious cycle. Targeting host cell processes for antiviral intervention is an unconventional approach that might limit antiviral resistance. Here we demonstrated that the proteasome inhibitor bortezomib, which is a clinically effective cancer drug, has the in vitro features of a promising anti-HSV therapeutic. Bortezomib inhibited HSV infection during the first hours of infection at nanomolar concentrations that were minimally cytotoxic. The mechanism of bortezomib’s inhibition of early HSV infection was to halt nucleocapsid transport to the nucleus and to stabilize the ND10 cellular defense complex. Bortezomib and acyclovir acted synergistically to inhibit HSV infection. Overall, we present evidence for the repurposing of bortezomib as a novel antiherpesviral agent and describe specific mechanisms of action.
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Computational drug repositioning requires assessment of the functional similarities among compounds. Here, we report a new method for measuring compound functional similarity based on gene expression data. This approach takes advantage of deep neural networks to learn an embedding that substantially denoises expression data, making replicates of the same compound more similar. Our method uses unlabeled data in the sense that it only requires compounds to be labeled by identity rather than detailed pharmacological information, which is often unavailable and costly to obtain. Similarity in the learned embedding space accurately predicted pharmacological similarities despite the lack of any such labels during training, and achieved substantially improved performance in comparison with previous similarity measures applied directly to gene expression measurements. Our method could identify drugs with shared therapeutic and biological targets even when the compounds were structurally dissimilar, thereby revealing previously unreported functional relationships between compounds. Thus, our approach provides an improved engine for drug repurposing based on expression data.
Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/L (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/L could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
Background: In late December, 2019, patients presenting with viral pneumonia due to an unidentified microbial agent were reported in Wuhan, China. A novel coronavirus was subsequently identified as the causative pathogen, provisionally named 2019 novel coronavirus (2019-nCoV). As of Jan 26, 2020, more than 2000 cases of 2019-nCoV infection have been confirmed, most of which involved people living in or visiting Wuhan, and human-to-human transmission has been confirmed. Methods: We did next-generation sequencing of samples from bronchoalveolar lavage fluid and cultured isolates from nine inpatients, eight of whom had visited the Huanan seafood market in Wuhan. Complete and partial 2019-nCoV genome sequences were obtained from these individuals. Viral contigs were connected using Sanger sequencing to obtain the full-length genomes, with the terminal regions determined by rapid amplification of cDNA ends. Phylogenetic analysis of these 2019-nCoV genomes and those of other coronaviruses was used to determine the evolutionary history of the virus and help infer its likely origin. Homology modelling was done to explore the likely receptor-binding properties of the virus. Findings: The ten genome sequences of 2019-nCoV obtained from the nine patients were extremely similar, exhibiting more than 99·98% sequence identity. Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%). Phylogenetic analysis revealed that 2019-nCoV fell within the subgenus Sarbecovirus of the genus Betacoronavirus, with a relatively long branch length to its closest relatives bat-SL-CoVZC45 and bat-SL-CoVZXC21, and was genetically distinct from SARS-CoV. Notably, homology modelling revealed that 2019-nCoV had a similar receptor-binding domain structure to that of SARS-CoV, despite amino acid variation at some key residues. Interpretation: 2019-nCoV is sufficiently divergent from SARS-CoV to be considered a new human-infecting betacoronavirus. Although our phylogenetic analysis suggests that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan might represent an intermediate host facilitating the emergence of the virus in humans. Importantly, structural analysis suggests that 2019-nCoV might be able to bind to the angiotensin-converting enzyme 2 receptor in humans. The future evolution, adaptation, and spread of this virus warrant urgent investigation. Funding: National Key Research and Development Program of China, National Major Project for Control and Prevention of Infectious Disease in China, Chinese Academy of Sciences, Shandong First Medical University.