scholarly journals Investigation of Thiocarbamates as Potential Inhibitors of the SARS-CoV-2 Mpro

2021 ◽  
Vol 14 (11) ◽  
pp. 1153
Author(s):  
Katarzyna Papaj ◽  
Patrycja Spychalska ◽  
Katarzyna Hopko ◽  
Patryk Kapica ◽  
Andre Fisher ◽  
...  
Keyword(s):  
Drug Design ◽  
Experimental Analysis ◽  
High Potential ◽  
Computational Studies ◽  
Lead Compounds ◽  
Microscale Thermophoresis ◽  
Potential Inhibitors

In the present study we tested, using the microscale thermophoresis technique, a small library of thionocarbamates, thiolocarbamates, sulfide and disulfide as potential lead compounds for SARS-CoV2 Mpro drug design. The successfully identified binder is a representative of the thionocarbamates group with a high potential for future modifications aiming for higher affinity and solubility. The experimental analysis was extended by computational studies that show insufficient accuracy of the simplest and widely applied approaches and underline the necessity of applying more advanced methods to properly evaluate the affinity of potential SARS-CoV2 Mpro binders.

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 Structure-based virtual screening for new lead compounds targeted Plasmodium α-tubulin

2021 ◽  
Vol 28 ◽  
pp. 135-139
Author(s):  
O. V. Rayevsky ◽  
O. M. Demchyk ◽  
P. A. Karpov ◽  
S. P. Ozheredov ◽  
S. I. Spivak ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Interaction ◽  
Specific Binding ◽  
Pharmacophore Model ◽  
Computational Prediction ◽  
Mitotic Apparatus ◽  
Ligand Interaction ◽  
Lead Compounds ◽  
Key Functional Groups ◽  
Potential Inhibitors

Aim. Search for new dinitroaniline and phosphorothioamide compounds, capable of selective binding with Plasmodium α-tubulin, affecting its mitotic apparatus. Methods. Structural biology methods of computational prediction of protein-ligand interaction: molecular docking, molecular dynamics and pharmacophore analysis. Selection of compounds based on pharmacophore characteristics and virtual screening results. Results. The protocol and required structural conditions for target (α-tubulin of P. falciparum) preparation and correct modeling of the ligand-protein interaction (docking and virtual screening) were developed. The generalized pharmacophore model of ligand-protein interaction and key functional groups of ligands responsible for specific binding were identified. Conclusions. Based on results of virtual screening, 22 commercial compounds were selected. Identified compounds proposed as potential inhibitors of Plasmodium mitotic machinery and the base of new antimalarial drugs. Keywords: malaria, Plasmodium, intermolecular interaction, dinitroaniline derived, phosphorothioamidate derived.

Development of potential inhibitors of cell division protein kinase 2 by ligand based drug design

2021 ◽  
pp. 1-10
Author(s):  
Vildan Enisoğlu Atalay ◽  
Büşra Savaş
Keyword(s):  
Hydrogen Bond ◽  
Cell Division ◽  
Drug Design ◽  
Discovery Studio ◽  
Cyclin Dependent Kinases ◽  
Hydrogen Bond Interactions ◽  
Damage Repair ◽  
Cancer Types ◽  
Potential Inhibitors ◽  
Cycle Regulation

Cyclin-dependent kinases (CDKs) are commonly known by their role in cell cycle regulation which affects cancer mechanism. In many cancer types, CDKs show extreme activity or CDK inhibiting proteins are dysfunctional. Specifically, CDK2 plays an indispensable role in cell division especially in the G1/S phase and DNA damage repair. Therefore, it is important to find new potential CDK2 inhibitors. In this study, ligand-based drug design is used to design new potential CDK2 inhibitors. Y8 L ligand is obtained from the X-ray crystal structure of human CDK2 (PDB ID: 2XNB) (www.pdb.org) and used as a structure model. By adding hydrophilic and hydrophobic groups to the structure, a training set of 36 molecules is generated. Each molecule examined with Spartan’14 and optimized structures are used for docking to CDK2 structure by AutoDock and AutoDock Vina programs. Ligand-amino acid interactions are analysed with Discovery Studio Visualizer. Van der Waals, Pi-Pi T-shaped, alkyl, pi-alkyl, conventional hydrogen bond and carbon-hydrogen bond interactions are observed. By docking results and viewed interactions, some molecules are identified and discussed as potential CDK2 inhibitors. Additionally, 8 different QSAR descriptors obtained from Spartan’14, Preadmet and ALOGPS 2.1 programs are investigated with multiple linear regulation (MLR) analysis with SPSS program for their impact on affinity value.

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