scholarly journals New Potential Drug Candidates Against SARS-CoV-2 Using Generative Model

2021 ◽  
Author(s):  
Madhusudan Verma
Keyword(s):  
Potential Drug ◽  
Q Learning ◽  
Learning Network ◽  
Drug Candidates ◽  
Lead Compounds ◽  
Main Protease ◽  
Variational Autoencoder ◽  
New Chemical Entities ◽  
Key Enzyme ◽  
Approved Drugs

<div>Since known approved drugs like liponavir and ritonavir failed to cure SARS-CoV-2 </div><div>infected patients, it is high time to generate new chemical entities against this virus. </div><div>3CL main protease acts as key enzyme for the growth of a virus which acts as </div><div>biocatalyst and helps to break protein and ultimately in the growth of coronavirus. </div><div>Based on a recently solved structure (PDB ID: 6LU7), we developed a novel </div><div>advanced deep Q-learning network with the fragment-based drug design </div><div>(ADQN-FBDD) along with variational autoencoder with KL annealing and circular </div><div>annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro. </div><div>Structure-based optimization policy (SBOP) is used in reinforcement learning. The </div><div>reason for using variational autoencoders is that it does not deviate much from actual </div><div>inhibitors, but since VAE suffers from KL diminishing we have used KL annealing </div><div>and circular annealing to address this issue. Researchers can use this compound as </div><div>potential drugs against SARS-CoV-2</div>

scholarly journals New Potential Drug Candidates Against SARS-CoV-2 Using Generative Model

2020 ◽  
Author(s):  
Madhusudan Verma
Keyword(s):  
Reinforcement Learning ◽  
Drug Design ◽  
Generative Model ◽  
Potential Drug ◽  
Q Learning ◽  
Learning Network ◽  
Drug Candidates ◽  
Lead Compounds ◽  
Variational Autoencoder

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2.

scholarly journals AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

2020 ◽  
Author(s):  
Bowen Tang ◽  
Fengming He ◽  
Dongpeng Liu ◽  
Meijuan Fang ◽  
Zhen Wu ◽  
...  
Keyword(s):  
Drug Design ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Lead Compounds ◽  
Main Protease ◽  
New Chemical Entities ◽  
Covalent Inhibitors ◽  
Key Enzyme ◽  
Approved Drugs ◽  
Aided Design

AbstractThe focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus. As a key enzyme in the life-cycle of coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from those lead compounds by our structure-based optimization policy (SBOP). All the 47 lead compounds directly from our AI-model and related derivatives based on SBOP are accessible in our molecular library at https://github.com/tbwxmu/2019-nCov. These compounds can be used as potential candidates for researchers in their development of drugs against SARS-CoV-2.

scholarly journals Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease

Science ◽  
2020 ◽  
Vol 368 (6497) ◽  
pp. 1331-1335 ◽  
Author(s):  
Wenhao Dai ◽  
Bing Zhang ◽  
Xia-Ming Jiang ◽  
Haixia Su ◽  
Jian Li ◽  
...  
Keyword(s):  
Antiviral Drug ◽  
X Ray ◽  
Drug Candidates ◽  
Lead Compounds ◽  
Main Protease ◽  
Pharmacokinetic Properties ◽  
Key Enzyme ◽  
Low Toxicity ◽  
Aldehyde Groups

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the etiological agent responsible for the global COVID-19 (coronavirus disease 2019) outbreak. The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a pivotal role in mediating viral replication and transcription. We designed and synthesized two lead compounds (11a and 11b) targeting Mpro. Both exhibited excellent inhibitory activity and potent anti–SARS-CoV-2 infection activity. The x-ray crystal structures of SARS-CoV-2 Mpro in complex with 11a or 11b, both determined at a resolution of 1.5 angstroms, showed that the aldehyde groups of 11a and 11b are covalently bound to cysteine 145 of Mpro. Both compounds showed good pharmacokinetic properties in vivo, and 11a also exhibited low toxicity, which suggests that these compounds are promising drug candidates.

scholarly journals Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Madhusudan Verma
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Drug Design ◽  
Q Learning ◽  
Learning Network ◽  
Lead Compounds ◽  
Variational Autoencoder ◽  
Potential Inhibitors

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue.

scholarly journals Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Madhusudan Verma ◽  
Deepanshu Bansal
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Drug Design ◽  
Q Learning ◽  
Learning Network ◽  
Lead Compounds ◽  
Variational Autoencoder ◽  
Potential Inhibitors

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2.

scholarly journals Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Madhusudan Verma
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Drug Design ◽  
Q Learning ◽  
Learning Network ◽  
Lead Compounds ◽  
Variational Autoencoder ◽  
Potential Inhibitors

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue.

scholarly journals Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Madhusudan Verma ◽  
Deepanshu Bansal
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Drug Design ◽  
Q Learning ◽  
Learning Network ◽  
Lead Compounds ◽  
Variational Autoencoder ◽  
Potential Inhibitors

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2.

scholarly journals Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Madhusudan Verma ◽  
Deepanshu Bansal
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Drug Design ◽  
Q Learning ◽  
Learning Network ◽  
Lead Compounds ◽  
Variational Autoencoder ◽  
Potential Inhibitors

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2.

scholarly journals Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence

2020 ◽  
Author(s):  
Madhusudan Verma ◽  
Deepanshu Bansal
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Drug Design ◽  
Q Learning ◽  
Learning Network ◽  
Lead Compounds ◽  
Variational Autoencoder ◽  
Potential Inhibitors

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2.

scholarly journals Screening of FDA Approved Drugs Against COVID-19 Main Protease: Coronavirus Disease

2020 ◽  
Author(s):  
Vijayakumar Balakrishnan ◽  
Karthik Lakshminarayanan
Keyword(s):  
Vaccine Development ◽  
New Drugs ◽  
World Health ◽  
Wuhan City ◽  
Drug Candidates ◽  
Human Contact ◽  
Main Protease ◽  
Potent Drug ◽  
Approved Drugs ◽  
Fda Approved Drugs

In the end of December 2019, a new strain of coronavirus was identified in the Wuhan city of Hubei province in China. Within a shorter period of time, an unprecedented outbreak of this strain was witnessed over the entire Wuhan city. This novel coronavirus strain was later officially renamed as COVID-19 (Coronavirus disease 2019) by the World Health Organization. The mode of transmission had been found to be human-to-human contact and hence resulted in a rapid surge across the globe where more than 1,100,000 people have been infected with COVID-19. In the current scenario, finding potent drug candidates for the treatment of COVID-19 has emerged as the most challenging task for clinicians and researchers worldwide. Identification of new drugs and vaccine development may take from a few months to years based on the clinical trial processes. To overcome the several limitations involved in identifying and bringing out potent drug candidates for treating COVID-19, in the present study attempts were made to screen the FDA approved drugs using High Throughput Virtual Screening (HTVS). The COVID-19 main protease (COVID-19 Mpro) was chosen as the drug target for which the FDA approved drugs were initially screened with HTVS. The drug candidates that exhibited favorable docking score, energy and emodel calculations were further taken for performing Induced Fit Docking (IFD) using Schrodinger&rsquo;s GLIDE. From the flexible docking results, the following four FDA approved drugs Sincalide, Pentagastrin, Ritonavir and Phytonadione were identified. In particular, Sincalide and Pentagastrin can be considered potential key players for the treatment of COVID-19 disease.

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