ArticleACS omega2020
Molecular Architect: A User-Friendly Workflow for Virtual Screening.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- MDCrow: automating molecular dynamics workflows with large language models.Machine learning: science and technology · 2026Article
- Computational Study of Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AnCurrent drug targets · 2026Article
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- Synthesis, Electrochemical Studies, Molecular Docking, and Biological Evaluation as an Antimicrobial Agent of 5-Amino-6-cyano-3-hydroxybenzo[ACS omega · 2022Article
- Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems.Pharmaceuticals (Basel, Switzerland) · 2022Article
- In vitro and in silico assessment of new beta amino ketones with antiplasmodial activity.Revista da Sociedade Brasileira de Medicina Tropical · 2022Article
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- Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches.International journal of molecular sciences · 2021Article
- Effect of drug metabolism in the treatment of SARS-CoV-2 from an entirely computational perspective.Scientific reports · 2021Article
- Computational Screening of Potential Inhibitors ofACS omega · 2021Article
- Computational evidence for nitro derivatives of quinoline and quinoline N-oxide as low-cost alternative for the treatment of SARS-CoV-2 infection.Scientific reports · 2021Article
- Dehydrobufotenin extracted from the Amazonian toadThe journal of venomous animals and toxins including tropical diseases · 2021Article
- Potential Inhibitors of Fascin From A Database of Marine Natural Products: A Virtual Screening and Molecular Dynamics Study.Frontiers in chemistry · 2021Article
- Ligand and structure-based virtual screening applied to the SARS-CoV-2 main protease: anFuture medicinal chemistry · 2020Article
- Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks.Pharmaceutics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.
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Registered trials
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