Computer-Aided Drug Design of Falcipain Inhibitors: Virtual Screening, Structure–Activity Relationships, Hydration Site Thermodynamics, and Reactivity Analysis

2012 ◽  
Vol 52 (3) ◽  
pp. 696-710 ◽  
Author(s):  
Falgun Shah ◽  
Jiri Gut ◽  
Jennifer Legac ◽  
Devleena Shivakumar ◽  
Woody Sherman ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Computer Aided Drug Design ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Computer Aided

scholarly journals COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

2018 ◽  
Vol 8 (5) ◽  
pp. 504-509 ◽  
Author(s):  
Surabhi Surabhi ◽  
BK Singh
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Design ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Drug Discovery And Development ◽  
Research Areas ◽  
Computer Aided ◽  
Pharmaceutical Industries

Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research. Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

The discovery of new acetylcholinesterase inhibitors derived from pharmacophore modeling, virtual screening, docking simulation and bioassays

2016 ◽  
Vol 12 (12) ◽  
pp. 3734-3742 ◽  
Author(s):  
Yunqin Zhang ◽  
Shuqun Zhang ◽  
Guowei Xu ◽  
Hui Yan ◽  
Yinglan Pu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Docking Simulation ◽  
Acetylcholinesterase Inhibitors ◽  
Pharmacophore Modeling ◽  
Computer Aided Drug Design ◽  
Ache Inhibitors ◽  
Computer Aided

Novel AChE inhibitors are discovered using computer aided drug design and bioassays.

scholarly journals A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

Marine Drugs ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. 633
Author(s):  
Susana P. Gaudêncio ◽  
Florbela Pereira
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Predictive Accuracy ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Classification Model ◽  
Protease Inhibition ◽  
Computer Aided Drug Design ◽  
Main Protease ◽  
Computer Aided

The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure–activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.

scholarly journals In Silico Exploration of Aryl Halides Analogues as CheckpointKinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study,and ADMET Screening

2019 ◽  
Vol 9 (1) ◽  
pp. 84-92 ◽  
Author(s):  
Adib Ghaleb ◽  
Adnane Aouidate ◽  
Mohammed Bouachrine ◽  
Tahar Lakhlifi ◽  
Abdelouhid Sbai
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
In Silico ◽  
3D Qsar ◽  
Structure Activity Relationship ◽  
Aryl Halides ◽  
Activity Relationship ◽  
Computer Aided Drug Design ◽  
Structure Activity ◽  
Computer Aided

Purpose: In this review, a set of aryl halides analogs were identified as potent checkpoint kinase1 (Chk1) inhibitors through a series of computer-aided drug design processes, to develop modelswith good predictive ability, highlight the important interactions between the ligand and theChk1 receptor protein and determine properties of the new proposed drugs as Chk1 inhibitorsagents.Methods: Three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling,molecular docking and absorption, distribution, metabolism, excretion and toxicity (ADMET)approaches are used to determine structure activity relationship and confirm the stableconformation on the receptor pocket.Results: The statistical analysis results of comparative -molecular field analysis (CoMFA) andcomparative molecular similarity indices analysis (CoMSIA) models that employed for a trainingset of 24 compounds gives reliable values of Q2 (0.70 and 0.94, respectively) and R2 (0.68 and0.96, respectively).Conclusion: Computer–aided drug design tools used to develop models that possess goodpredictive ability, and to determine the stability of the observed and predicted molecules in thereceptor pocket, also in silico of pharmacokinetic (ADMET) results shows good properties andbioavailability for these new proposed Chk1 inhibitors agents.

Consensus Docking in Drug Discovery

2020 ◽  
Vol 16 (3) ◽  
pp. 182-190 ◽  
Author(s):  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Binding Mode ◽  
Point Of View ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Computer Aided Drug Design ◽  
Successful Case ◽  
Computer Aided ◽  
High Level

Background: Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets. Methods: In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose. Results: The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus. Conclusion: In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Structure–activity relationship analysis of a series of nonsteroidal analogues as androgen receptor antagonists

2021 ◽  
Author(s):  
Miao Yuan ◽  
Ping Cheng ◽  
Shuping Zhang
Keyword(s):  
Androgen Receptor ◽  
Drug Design ◽  
Large Scale ◽  
Activity Relationship ◽  
Computer Aided Drug Design ◽  
Design Technology ◽  
Relationship Analysis ◽  
Structure Activity ◽  
Computer Aided ◽  
Androgen Receptor Antagonists

Computer-aided drug design technology was used to screen drugs in large-scale and to accelerate the progress of drug design of nonsteroidal compounds deriving from the hybridization of FDA-approved Enzalutamide and Abiraterone.

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