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 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 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 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 Cheminformatic Characterization of Natural Antimicrobial Products for the Development of New Lead Compounds

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3970
Author(s):  
Samson Olaitan Oselusi ◽  
Alan Christoffels ◽  
Samuel Ayodele Egieyeh
Keyword(s):  
Antimicrobial Resistance ◽  
Antimicrobial Agents ◽  
Multidrug Resistant ◽  
Potential Drug ◽  
Natural Antimicrobial ◽  
Drug Candidates ◽  
Lead Compounds ◽  
Multidrug Resistant Pathogens

The growing antimicrobial resistance (AMR) of pathogenic organisms to currently prescribed drugs has resulted in the failure to treat various infections caused by these superbugs. Therefore, to keep pace with the increasing drug resistance, there is a pressing need for novel antimicrobial agents, especially from non-conventional sources. Several natural products (NPs) have been shown to display promising in vitro activities against multidrug-resistant pathogens. Still, only a few of these compounds have been studied as prospective drug candidates. This may be due to the expensive and time-consuming process of conducting important studies on these compounds. The present review focuses on applying cheminformatics strategies to characterize, prioritize, and optimize NPs to develop new lead compounds against antimicrobial resistance pathogens. Moreover, case studies where these strategies have been used to identify potential drug candidates, including a few selected open-access tools commonly used for these studies, are briefly outlined.

scholarly journals A review of motion planning algorithms for intelligent robots

2021 ◽  
Author(s):  
Chengmin Zhou ◽  
Bingding Huang ◽  
Pasi Fränti
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Q Learning ◽  
Learning Network ◽  
Gradient Algorithms ◽  
Optimal Value ◽  
Policy Gradient ◽  
Planning Algorithms

AbstractPrinciples of typical motion planning algorithms are investigated and analyzed in this paper. These algorithms include traditional planning algorithms, classical machine learning algorithms, optimal value reinforcement learning, and policy gradient reinforcement learning. Traditional planning algorithms investigated include graph search algorithms, sampling-based algorithms, interpolating curve algorithms, and reaction-based algorithms. Classical machine learning algorithms include multiclass support vector machine, long short-term memory, Monte-Carlo tree search and convolutional neural network. Optimal value reinforcement learning algorithms include Q learning, deep Q-learning network, double deep Q-learning network, dueling deep Q-learning network. Policy gradient algorithms include policy gradient method, actor-critic algorithm, asynchronous advantage actor-critic, advantage actor-critic, deterministic policy gradient, deep deterministic policy gradient, trust region policy optimization and proximal policy optimization. New general criteria are also introduced to evaluate the performance and application of motion planning algorithms by analytical comparisons. The convergence speed and stability of optimal value and policy gradient algorithms are specially analyzed. Future directions are presented analytically according to principles and analytical comparisons of motion planning algorithms. This paper provides researchers with a clear and comprehensive understanding about advantages, disadvantages, relationships, and future of motion planning algorithms in robots, and paves ways for better motion planning algorithms in academia, engineering, and manufacturing.

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