In silico rational design and virtual screening of antixoidant tripeptides based on 3D-QSAR modeling

2019 ◽  
Vol 1193 ◽  
pp. 223-230 ◽  
Author(s):  
Haiqiong Guo ◽  
Yuxuan Wang ◽  
Qingxiu He ◽  
Yuping Zhang ◽  
Yong Hu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
In Silico ◽  
Rational Design ◽  
3D Qsar ◽  
Qsar Modeling

scholarly journals In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

ACS Omega ◽  
2020 ◽  
Vol 5 (11) ◽  
pp. 5951-5958 ◽  
Author(s):  
Mehri Mahmoodi-Reihani ◽  
Fatemeh Abbasitabar ◽  
Vahid Zare-Shahabadi
Keyword(s):  
Virtual Screening ◽  
In Silico ◽  
Rational Design ◽  
Bioactive Peptides ◽  
Qsar Modeling

Rational Design of Macrolides by Virtual Screening of Combinatorial Libraries Generated through in Silico Manipulation of Polyketide Synthases

2006 ◽  
Vol 49 (6) ◽  
pp. 2077-2087 ◽  
Author(s):  
Sergey B. Zotchev ◽  
Alla V. Stepanchikova ◽  
Anastasia P. Sergeyko ◽  
Boris N. Sobolev ◽  
Dmitrii A. Filimonov ◽  
...  
Keyword(s):  
Virtual Screening ◽  
In Silico ◽  
Rational Design ◽  
Combinatorial Libraries ◽  
Polyketide Synthases

Molecular Modeling Studies on 2,4-Disubstituted Imidazopyridines as Anti-Malarials: Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, In-Silico ADMET and Theoretical Analysis

2021 ◽  
pp. 2150012
Author(s):  
Suraj N. Mali ◽  
Anima Pandey
Keyword(s):  
Molecular Docking ◽  
Molecular Modeling ◽  
Virtual Screening ◽  
In Silico ◽  
Energy Gap ◽  
3D Qsar ◽  
Modeling Studies ◽  
In Vitro Investigation ◽  
In Silico Admet ◽  
Molecular Modeling Studies

Malarial parasites have been reported for moderate-high resistance towards classical antimalarial agents and henceforth development of newer novel chemical entities targeting multiple targets rather than targeting single target will be a highly promising strategy in antimalarial drug discovery. Herein, we carried out molecular modeling studies on 2,4-disubstituted imidazopyridines as anti-hemozoin formation inhibitors by using Schrödinger’s molecular modeling package (2020_4). We have developed statistically robust atom-based 3D-QSAR model (training set, [Formula: see text]; test set, [Formula: see text]; [Formula: see text] [Formula: see text]; root-mean-square error, [Formula: see text]; standard deviation, [Formula: see text]). Our molecular docking, in-silico ADMET analysis showed that dataset molecule 37, has highly promising results. Our ligand-based virtual screening resulted in top five ZINC hits, among them ZINC73737443 hit was observed with lesser energy gap, i.e. 7.85[Formula: see text]eV, higher softness value (0.127[Formula: see text]eV), and comparatively good docking score of [Formula: see text]10.2[Formula: see text]kcal/mol. Our in-silico analysis for a proposed hit, ZINC73737443 showed that this molecule has good ADMET, in-silico nonames toxic as well as noncarcinogenic profile. We believe that further experimental as well as the in-vitro investigation will throw more lights on the identification of ZINC73737443 as a potential antimalarial agent.

scholarly journals The Integration of Pharmacophore-Based 3D QSAR Modeling and Virtual Screening in Identification of Natural Product Inhibitors against SARS-CoV-2

2020 ◽  
Author(s):  
Samira Norouzi ◽  
Maryam Farahani ◽  
Samad Nejad Ebrahimi
Keyword(s):  
Virtual Screening ◽  
3D Structure ◽  
3D Qsar ◽  
Qsar Model ◽  
Docking Studies ◽  
Molecular Characteristics ◽  
Qsar Modeling ◽  
Data Set ◽  
Public Health Emergencies ◽  
Current Outbreak

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.

2D- and 3D-QSAR Modeling of Imidazole-Based Glutaminyl Cyclase Inhibitors

2019 ◽  
Vol 15 ◽  
Author(s):  
Omar Husham Ahmed Al-Attraqchi ◽  
Katharigatta N. Venugopala
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Virtual Screening ◽  
Neurodegenerative Disorder ◽  
3D Qsar ◽  
Biological Evaluation ◽  
Qsar Modeling ◽  
Glutaminyl Cyclase ◽  
Validation Parameters ◽  
Qsar Models

Background: Glutaminyl cyclase (QC) is a novel target in the battle against Alzheimer’s disease, a highly prevalent neurodegenerative disorder. QC inhibitors have the potential to be developed as therapeutically useful anti-Alzheimer’s disease agents. Methods: Linear and non-linear 2D-quantitative structure–activity relationship (QSAR) models were developed using stepwise-multiple linear regression (S-MLR) and neural networks. Partial least squares (PLS) method was used to develop a 3D-QSAR model. Also, the developed models were used in a virtual screening of the ZINC database to identify potential QC inhibitors. Results: The 2D neural network model showed superior predictive ability, as demonstrated by the validation parameters R2 = 0.933, Q2 = 0.886 and R2pred = 0.911. The 3D-QSAR model’s steric and electrostatic fields’ isocontour maps were visualized and revealed important structural requirements associated with good activity. The virtual screening identified six compounds as potentially active QC inhibitors with improved pharmacokinetic profiles. Conclusion: The developed QSAR models can be used to predict the activity of compounds not yet synthesized and prioritize their synthesis and biological evaluation. Also, potentially active QC inhibitors have been identified with attractive lead-like properties that can be used to develop anti-Alzheimer’s disease agents.

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