scholarly journals Open Source Drug Discovery with Bioclipse

2013 ◽  
Vol 12 (18) ◽  
pp. 1980-1986
Author(s):  
Ola Spjuth ◽  
Jonathan Alvarsson ◽  
Egon Willighagen ◽  
Lars Carlsson ◽  
Valentin Georgiev ◽  
...  
Keyword(s):  
Decision Support ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Knowledge Discovery ◽  
Open Source ◽  
Chemical Structures ◽  
Related Information ◽  
Computational Pharmacology ◽  
Qspr Modeling ◽  
Discovery And Integration

We present the open source components for drug discovery that has been developed and integrated into the graphical workbench Bioclipse. Building on a solid open source cheminformatics core, Bioclipse has advanced functionality for managing and visualizing chemical structures and related information. The features presented here include QSAR/QSPR modeling, various predictive solutions such as decision support for chemical liability assessment, site-ofmetabolism prediction, virtual screening, and knowledge discovery and integration. We demonstrate the utility of the described tools with examples from computational pharmacology, toxicology, and ADME. Bioclipse is used in both academia and industry, and is a good example of open source leading to new solutions for drug discovery.

scholarly journals InterLig: a fast and accurate software for ligand-based virtual screening

2019 ◽  
Author(s):  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Small Molecules ◽  
Open Source ◽  
Computational Methods ◽  
New Method ◽  
Scaffold Hopping ◽  
Protein Target ◽  
The Past ◽  
3D Methods

AbstractIn the past few years, drug discovery processes have been relying more and more on computational methods to sift out the most promising molecules before time and resources are spent to test them in experimental settings. Whenever the protein target of a given disease is not known, it becomes fundamental to have accurate methods for ligand-based Virtual Screening, which compare known active molecules against vast libraries of candidate compounds. Recently, 3D-based similarity methods have been developed that are capable of scaffold-hopping and to superimpose matching molecules. Here, we present InterLig, a new method for the comparison and superposition of small molecules based on 3D, topologically-independent alignments of atoms. We test InterLig on a standard benchmark and show that it compares favorably to the best currently available 3D methods.InterLig is open source and is available to everyone at: http://wallnerlab.org/interlig.

Dose COVID-19 Uncovered a New Feature of Metronidazole Drug?

2020 ◽  
Author(s):  
Mohammad Seyedhamzeh ◽  
Bahareh Farasati Far ◽  
Mehdi Shafiee Ardestani ◽  
Shahrzad Javanshir ◽  
Fatemeh Aliabadi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Active Sites ◽  
Initial Plasma ◽  
New Feature ◽  
Metronidazole Benzoate ◽  
Global Health Problem ◽  
A Current ◽  
Pro Inflammatory Cytokines

Studies of coronavirus disease 2019 (COVID-19) as a current global health problem shown the initial plasma levels of most pro-inflammatory cytokines increased during the infection, which leads to patient countless complications. Previous studies also demonstrated that the metronidazole (MTZ) administration reduced related cytokines and improved treatment in patients. However, the effect of this drug on cytokines has not been determined. In the present study, the interaction of MTZ with cytokines was investigated using molecular docking as one of the principal methods in drug discovery and design. According to the obtained results, the IL12-metronidazole complex is more stable than other cytokines, and an increase in the surface and volume leads to prevent to bind to receptors. Moreover, ligand-based virtual screening of several libraries showed metronidazole phosphate, metronidazole benzoate, 1-[1-(2-Hydroxyethyl)-5- nitroimidazol-2-yl]-N-methylmethanimine oxide, acyclovir, and tetrahydrobiopterin (THB or BH4) like MTZ by changing the surface and volume prevents binding IL-12 to the receptor. Finally, the inhibition of the active sites of IL-12 occurred by modifying the position of the methyl and hydroxyl functional groups in MTZ. <br>

Unzipping Natural Products: Improved Natural Product Structure Predictions by Ensemble Modeling and Fingerprint Matching

2018 ◽  
Author(s):  
William A. Shirley ◽  
Brian P. Kelley ◽  
Yohann Potier ◽  
John H. Koschwanez ◽  
Robert Bruccoleri ◽  
...  
Keyword(s):  
Natural Products ◽  
Open Source ◽  
Natural Product ◽  
Gene Cluster ◽  
Public Domain ◽  
Product Structure ◽  
Fingerprint Matching ◽  
Ensemble Modeling ◽  
Chemical Structures ◽  
Source Gene

This pre-print explores ensemble modeling of natural product targets to match chemical structures to precursors found in large open-source gene cluster repository antiSMASH. Commentary on method, effectiveness, and limitations are enclosed. All structures are public domain molecules and have been reviewed for release.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Structure-Based Virtual Screening and Biochemical Evaluation for the Identification of Novel Trypanosoma brucei Aldolase Inhibitors

2018 ◽  
Vol 18 (5) ◽  
pp. 397-405 ◽  
Author(s):  
Leonardo L.G. Ferreira ◽  
Rafaela S. Ferreira ◽  
David L. Palomino ◽  
Adriano D. Andricopulo
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Trypanosoma Brucei ◽  
Enzyme Inhibitor ◽  
Practical Knowledge ◽  
Glycolytic Enzyme ◽  
Structure Based Drug Design ◽  
Biochemical Evaluation ◽  
Dynamics Simulations ◽  
Fructose 1,6 Bisphosphate

Introduction: The glycolytic enzyme fructose-1,6-bisphosphate aldolase is a validated molecular target in human African trypanosomiasis (HAT) drug discovery, a neglected tropical disease (NTD) caused by the protozoan Trypanosoma brucei. Herein, a structure-based virtual screening (SBVS) approach to the identification of novel T. brucei aldolase inhibitors is described. Distinct molecular docking algorithms were used to screen more than 500,000 compounds against the X-ray structure of the enzyme. This SBVS strategy led to the selection of a series of molecules which were evaluated for their activity on recombinant T. brucei aldolase. The effort led to the discovery of structurally new ligands able to inhibit the catalytic activity of the enzyme. Results: The predicted binding conformations were additionally investigated in molecular dynamics simulations, which provided useful insights into the enzyme-inhibitor intermolecular interactions. Conclusion: The molecular modeling results along with the enzyme inhibition data generated practical knowledge to be explored in further structure-based drug design efforts in HAT drug discovery.

scholarly journals Toward a Coronavirus Knowledge Graph

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 998
Author(s):  
Peng Zhang ◽  
Yi Bu ◽  
Peng Jiang ◽  
Xiaowen Shi ◽  
Bing Lun ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Angiotensin Converting Enzyme ◽  
Information Sources ◽  
Knowledge Graph ◽  
Converting Enzyme ◽  
Related Information ◽  
Relationship Types ◽  
Entity Disambiguation ◽  
Public Datasets ◽  
Biological Entities

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG’s usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.

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