scholarly journals Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

2020 ◽  
Author(s):  
Alex Zhavoronkov ◽  
Vladimir Aladinskiy ◽  
Alexander Zhebrak ◽  
Bogdan Zagribelnyy ◽  
Victor Terentiev ◽  
...  
Keyword(s):  
Homology Modelling ◽  
Drug Repurposing ◽  
Hiv Protease ◽  
Learning Approaches ◽  
Protein Targets ◽  
Chemical Libraries ◽  
Internal Resources ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Novel Drug

<div> <div> <div> <p>The emergence of the 2019 novel coronavirus (2019-nCoV), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important 2019-nCoV protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of 2019-nCoV and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked- based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches are being published at www.insilico.com/ncov-sprint/ and will be continuously updated. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. </p> </div> </div> </div>

scholarly journals Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

2020 ◽  
Author(s):  
Alex Zhavoronkov ◽  
Vladimir Aladinskiy ◽  
Alexander Zhebrak ◽  
Bogdan Zagribelnyy ◽  
Victor Terentiev ◽  
...  
Keyword(s):  
Homology Modelling ◽  
Drug Repurposing ◽  
Hiv Protease ◽  
Learning Approaches ◽  
Protein Targets ◽  
Chemical Libraries ◽  
Internal Resources ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Novel Drug

<div> <div> <p>The emergence of the 2019 novel coronavirus (COVID-19), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important COVID-19 protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar, which is far from ideal. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of COVID-19 and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked-based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches were published at <a href="http://www.insilico.com/ncov-sprint/">www.insilico.com/ncov-sprint/</a>. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. <br></p> </div> </div>

scholarly journals Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

2020 ◽  
Author(s):  
Alex Zhavoronkov ◽  
Vladimir Aladinskiy ◽  
Alexander Zhebrak ◽  
Bogdan Zagribelnyy ◽  
Victor Terentiev ◽  
...  
Keyword(s):  
Homology Modelling ◽  
Drug Repurposing ◽  
Hiv Protease ◽  
Learning Approaches ◽  
Protein Targets ◽  
Chemical Libraries ◽  
Internal Resources ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Novel Drug

<div> <div> <p>The emergence of the 2019 novel coronavirus (COVID-19), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important COVID-19 protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar, which is far from ideal. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of COVID-19 and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked-based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches were published at <a href="http://www.insilico.com/ncov-sprint/">www.insilico.com/ncov-sprint/</a>. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. <br></p> </div> </div>

scholarly journals Repurposing drugs for treatment of SARS-CoV-2 infection: Computational design insights into mechanisms of action

2020 ◽  
Author(s):  
Shubhangi Kandwal ◽  
Darren Fayne
Keyword(s):  
Human Life ◽  
Drug Repurposing ◽  
Computational Design ◽  
Therapeutic Interventions ◽  
The Novel ◽  
Protein Targets ◽  
Novel Coronavirus ◽  
Approved Drugs

Abstract The COVID-19 pandemic has negatively affected human life globally. It has led to economic crises and health emergencies across the world, spreading rapidly among the human population and has caused many deaths. Currently, there are no treatments available for COVID-19 so there is an urgent need to develop therapeutic interventions that could be used against the novel coronavirus infection. In this research, we used computational drug design technologies to repurpose existing drugs as inhibitors of SARS-CoV-2 viral proteins. The Broad Institute’s Drug Repurposing Hub consists of in-development/approved drugs and was computationally screened to identify potential hits which could inhibit protein targets encoded by the SARS-CoV-2 genome. By virtually screening the Broad collection, using rationally designed pharmacophore features, we identified molecules which may be repurposed against viral nucleocapsid and non-structural proteins. The pharmacophore features were generated after careful visualisation of the interactions between co-crystalised ligands and the protein binding site. The ChEMBL database was used to determine the compound’s level of inhibition of SARS-CoV-2 and correlate the predicted viral protein target with whole virus in vitro data. The results from this study may help to accelerate drug development against COVID-19 and the hit compounds should be progressed through further in vitro and in vivo studies on SARS-CoV-2.

scholarly journals Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianfang Tang ◽  
Lijun Cai ◽  
Yajie Meng ◽  
JunLin Xu ◽  
Changcheng Lu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
De Novo ◽  
Characteristic Curve ◽  
Drug Repurposing ◽  
Factorization Method ◽  
Therapeutic Drugs ◽  
Indicator Matrix ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Non Negative Matrix Factorization

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

scholarly journals Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using The CANDO Platform: Implications for Drug Design

2018 ◽  
Author(s):  
William Mangione ◽  
Ram Samudrala
Keyword(s):  
Drug Design ◽  
Drug Repurposing ◽  
Biomedical Literature ◽  
Learning Approaches ◽  
Drug Candidates ◽  
Protein Library ◽  
Fold Reduction ◽  
Ligand Interactions ◽  
Novel Drug

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100&ndash;1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

scholarly journals In Silico Identification and Docking-Based Drug Repurposing Against the Main Protease of SARS-CoV-2, Causative Agent of COVID-19

2020 ◽  
Author(s):  
Yogesh Kumar ◽  
Harvijay Singh
Keyword(s):  
Virtual Screening ◽  
Active Site ◽  
Viral Infections ◽  
Drug Repurposing ◽  
Docking Study ◽  
Docking Studies ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs

<div>The rapidly enlarging COVID-19 pandemic caused by novel SARS-coronavirus 2 is a global</div><div>public health emergency of unprecedented level. Therefore the need of a drug or vaccine that</div><div>counter SARS-CoV-2 is an utmost requirement at this time. Upon infection the ssRNA genome</div><div>of SARS-CoV-2 is translated into large polyprotein which further processed into different</div><div>nonstructural proteins to form viral replication complex by virtue of virus specific proteases:</div><div>main protease (3-CL protease) and papain protease. This indispensable function of main protease</div><div>in virus replication makes this enzyme a promising target for the development of inhibitors and</div><div>potential treatment therapy for novel coronavirus infection. The recently concluded α-ketoamide</div><div>ligand bound X-ray crystal structure of SARS-CoV-2 Mpro (PDB ID: 6Y2F) from Zhang et al.</div><div>has revealed the potential inhibitor binding mechanism and the determinants responsible for</div><div>involved molecular interactions. Here, we have carried out a virtual screening and molecular</div><div>docking study of FDA approved drugs primarily targeted for other viral infections, to investigate</div><div>their binding affinity in Mpro active site. Virtual screening has identified a number of antiviral</div><div>drugs, top ten of which on the basis of their bending energy score are further examined through </div><div>molecular docking with Mpro. Docking studies revealed that drug Lopinavir-Ritonavir, Tipranavir</div><div>and Raltegravir among others binds in the active site of the protease with similar or higher</div><div>affinity than the crystal bound inhibitor α-ketoamide. However, the in-vitro efficacies of the drug</div><div>molecules tested in this study, further needs to be corroborated by carrying out biochemical and</div><div>structural investigation. Moreover, this study advances the potential use of existing drugs to be</div><div>investigated and used to contain the rapidly expanding SARS-CoV-2 infection.</div>

scholarly journals A proteomic model of SARS-COV2 infection by comparing the interactomes of BRD4 with BET-inhibition and SARS-COV2 viral proteins – implications for re-purposing approved drugs or ubiquitin-mediated degradation of select candidates

2020 ◽  
Author(s):  
GIRISH NALLUR
Keyword(s):  
Molecular Mechanisms ◽  
Viral Proteins ◽  
Disease Diagnosis ◽  
Cellular Protein ◽  
Interacting Protein ◽  
Protein Targets ◽  
Us Fda ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Diagnosis Therapy

Abstract The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected 2,029,930 people worldwide and caused 136,320 deaths. Consequently, the hunt for drugs showing efficacy against this deadly disease, or vaccines for prevention, are being intensely investigated. Unfortunately, there is a scarcity of research data on the molecular mechanisms of SARS-CoV-2 infection for quickly finding effective therapies, or repurposing existing drugs approved by the US FDA. This report models existing knowledge of SARS-COV2 viral proteins and the cellular proteins they interact with by comparisons with BRD4 interacting proteins identified from B cells, with or without BET inhibition. The E protein of SARS-COV2 interacts with BRD4, and the Spike (S) protein with CANX. Extensive similarities were observed with published cellular interactants of 13 SARS-COV2 proteins resulting in 47 BRD4-interacting protein candidates, with or without BET inhibition. 61 cellular protein targets and 132 FDA approved drugs which use these proteins as targets are proposed, which can be investigated for efficacy against SARS-COV2 infections. The implications to SARS-COV2 disease diagnosis, therapy and vaccine creation are discussed.

scholarly journals Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

2020 ◽  
Author(s):  
Scott Bembenek
Keyword(s):  
Molecular Modeling ◽  
Therapeutic Targets ◽  
Drug Repurposing ◽  
Preclinical Research ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Usual Process ◽  
Potential Inhibitors ◽  
Computational Platform

<p>The recent<b> </b>outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (M<sup>pro</sup>, 3CL<sup>pro</sup>). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.</p>

Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

2020 ◽  
Author(s):  
Scott Bembenek
Keyword(s):  
Molecular Modeling ◽  
Therapeutic Targets ◽  
Drug Repurposing ◽  
Preclinical Research ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Usual Process ◽  
Potential Inhibitors ◽  
Computational Platform

<p>The recent<b> </b>outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (M<sup>pro</sup>, 3CL<sup>pro</sup>). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.</p>

Drug Discovery by Drug Repurposing: Combating COVID-19 in the 21st Century

2020 ◽  
Vol 20 ◽  
Author(s):  
Nitesh Sanghai ◽  
Kashfia Shafiq ◽  
Geoffrey K. Tranmer
Keyword(s):  
Drug Discovery ◽  
Drug Repurposing ◽  
Compassionate Use ◽  
Drug Candidates ◽  
Working Day ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Day By Day ◽  
Potential Use

: Due to the rapidly developing nature of the current COVID-19 outbreak and its almost immediate humanitarian and economic toll, coronavirus drug discovery efforts have largely focused on generating potential COVID-19 drug candidates as quickly as possible. Globally, scientists are working day and night to find the best possible solution to treat the deadly virus. During the first few months of 2020, the SARS-CoV-2 outbreak quickly developed into a pandemic, with a mortality rate that was increasing at an exponential rate day by day. As a result, scientists have turned to a drug repurposing approach, to rediscover the potential use and benefits of existing approved drugs. Currently, there is no single drug approved by the U.S. Food and Drug Administration (FDA), for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) that causes COVID-19. Based on only in-vitro studies, several active drugs are already in the clinical pipeline, made possible by following the compassionate use of medicine protocols. This method of repurposing and the use of existing molecules like Remdesivir (GS-5734), Chloroquine, Hydroxychloroquine, etc. has proven to be a landmark in the field of drug rediscovery. In this review article we will discuss the repurposing of medicines for treating the deadly novel coronavirus (SARS-CoV-2).

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