scholarly journals Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N -acetyltransferases

2009 ◽  
Vol 7 (43) ◽  
pp. 335-342 ◽  
Author(s):  
Pedro J. Ballester ◽  
Isaac Westwood ◽  
Nicola Laurieri ◽  
Edith Sim ◽  
W. Graham Richards
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Small Molecules ◽  
Shape Recognition ◽  
Molecular Targets ◽  
Efficient Manner ◽  
Hit Rate ◽  
Shape Complementarity ◽  
Outstanding Result ◽  
Novel Structures

There is currently a shortage of chemical molecules that can be used as bioactive probes to study molecular targets and potentially as starting points for drug discovery. One inexpensive way to address this problem is to use computational methods to screen a comprehensive database of small molecules to discover novel structures that could lead to alternative and better bioactive probes. Despite that pleasing logic the results have been somewhat mixed. Here we describe a virtual screening technique based on ligand–receptor shape complementarity, Ultrafast Shape Recognition (USR). USR is specifically applied to identify novel inhibitors of arylamine N -acetyltransferases by computationally screening almost 700 million molecular conformers in a time- and resource-efficient manner. A small number of the predicted active compounds were purchased and tested obtaining a confirmed hit rate of 40 per cent which is an outstanding result for a prospective virtual screening.

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.

scholarly journals New Approaches to Difficult Drug Targets: The Phosphatase Story

2017 ◽  
Vol 22 (9) ◽  
pp. 1071-1083 ◽  
Author(s):  
John S. Lazo ◽  
Kelley E. McQueeney ◽  
Elizabeth R. Sharlow
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Protein Tyrosine Phosphatase ◽  
Drug Targets ◽  
Tyrosine Phosphatase ◽  
Molecular Targets ◽  
Protein Tyrosine Phosphatases ◽  
Protein Tyrosine ◽  
Further Development ◽  
Enzyme Class

The drug discovery landscape is littered with promising therapeutic targets that have been abandoned because of insufficient validation, historical screening failures, and inferior chemotypes. Molecular targets once labeled as “undruggable” or “intractable” are now being more carefully interrogated, and while they remain challenging, many target classes are appearing more approachable. Protein tyrosine phosphatases represent an excellent example of a category of molecular targets that have emerged as druggable, with several small molecules and antibodies recently becoming available for further development. In this review, we examine some of the diseases that are associated with protein tyrosine phosphatase dysfunction and use some prototype contemporary strategies to illustrate approaches that are being used to identify small molecules targeting this enzyme class.

scholarly journals InterLig: improved ligand-based virtual screening using topologically independent structural alignments

2020 ◽  
Vol 36 (10) ◽  
pp. 3266-3267
Author(s):  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Small Molecules ◽  
Supplementary Information ◽  
Scaffold Hopping ◽  
Supplementary Data ◽  
Protein Target ◽  
The Past ◽  
3D Methods ◽  
Structural Alignments

Abstract Motivation In 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 compares 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. Results Here, we present InterLig, a new method for the comparison and superposition of small molecules using 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. Availability and implementation The program is available from http://wallnerlab.org/InterLig. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Accelerating Drug Discovery and Repurposing by Combining Transcriptional Signature Connectivity with Docking

2020 ◽  
Author(s):  
Alexander W. Thorman ◽  
James Reigle ◽  
Somchai Chutipongtanate ◽  
Behrouz Shamsaei ◽  
Marcin Pilarczyk ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Small Molecules ◽  
False Positive ◽  
Target Gene ◽  
Treatment Options ◽  
Small Subset ◽  
Chemical Similarity ◽  
Docking Simulations ◽  
Transcriptional Signature

AbstractThe development of targeted treatment options for precision medicine is hampered by a slow and costly process of drug screening. While small molecule docking simulations are often applied in conjunction with cheminformatic methods to reduce the number of candidate molecules to be tested experimentally, the current approaches suffer from high false positive rates and are computationally expensive. Here, we present a novel in silico approach for drug discovery and repurposing, dubbed connectivity enhanced Structure Activity Relationship (ceSAR) that improves on current methods by combining docking and virtual screening approaches with pharmacogenomics and transcriptional signature connectivity analysis. ceSAR builds on the landmark LINCS library of transcriptional signatures of over 20,000 drug-like molecules and ~5,000 gene knock-downs (KDs) to connect small molecules and their potential targets. For a set of candidate molecules and specific target gene, candidate molecules are first ranked by chemical similarity to their ‘concordant’ LINCS analogs that share signature similarity with a knock-down of the target gene. An efficient method for chemical similarity search, optimized for sparse binary fingerprints of chemical moieties, is used to enable fast searches for large libraries of small molecules. A small subset of candidate compounds identified in the first step is then re-scored by combining signature connectivity with docking simulations. On a set of 20 DUD-E benchmark targets with LINCS KDs, the consensus approach reduces significantly false positive rates, improving the median precision 3-fold over docking methods at the extreme library reduction. We conclude that signature connectivity and docking provide complementary signals, offering an avenue to improve the accuracy of virtual screening while reducing run times by multiple orders of magnitude.

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.

Computer-Assisted Drug Virtual Screening Based on the Natural Product Databases

2019 ◽  
Vol 20 (4) ◽  
pp. 293-301 ◽  
Author(s):  
Baoyu Yang ◽  
Jing Mao ◽  
Bing Gao ◽  
Xiuli Lu
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Small Molecules ◽  
Natural Product ◽  
Drug Screening ◽  
Biologically Active ◽  
Computer Assisted ◽  
Toxicity Prediction ◽  
Protein Target

Background:Computer-assisted drug virtual screening models the process of drug screening through computer simulation technology, by docking small molecules in some of the databases to a certain protein target. There are many kinds of small molecules databases available for drug screening, including natural product databases.Methods:Plants have been used as a source of medication for millennia. About 80% of drugs were either natural products or related analogues by 1990, and many natural products are biologically active and have favorable absorption, distribution, metabolization, excretion, and toxicology.Results:In this paper, we review the natural product databases’ contributions to drug discovery based on virtual screening, focusing particularly on the introductions of plant natural products, microorganism natural product, Traditional Chinese medicine databases, as well as natural product toxicity prediction databases.Conclusion:We highlight the applications of these databases in many fields of virtual screening, and attempt to forecast the importance of the natural product database in next-generation drug discovery.

scholarly journals The Light and Dark Sides of Virtual Screening: What Is There to Know?

2019 ◽  
Vol 20 (6) ◽  
pp. 1375 ◽  
Author(s):  
Aleix Gimeno ◽  
María Ojeda-Montes ◽  
Sarah Tomás-Hernández ◽  
Adrià Cereto-Massagué ◽  
Raúl Beltrán-Debón ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Small Molecules ◽  
Bioactive Compounds ◽  
In Silico ◽  
Research Team ◽  
Computational Tools ◽  
Private And Public ◽  
New Methodologies ◽  
Experienced Researcher

Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

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>

Aim for the Readers! Bromodomains As New Targets Against Chagas’ Disease

2019 ◽  
Vol 26 (36) ◽  
pp. 6544-6563
Author(s):  
Victoria Lucia Alonso ◽  
Luis Emilio Tavernelli ◽  
Alejandro Pezza ◽  
Pamela Cribb ◽  
Carla Ritagliati ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Chagas Disease ◽  
Causative Agent ◽  
Current Knowledge ◽  
Sequence Similarity ◽  
Lysine Acetylation ◽  
Specific Manner ◽  
Lysine Residues ◽  
Antiparasitic Drugs

Bromodomains recognize and bind acetyl-lysine residues present in histone and non-histone proteins in a specific manner. In the last decade they have raised as attractive targets for drug discovery because the miss-regulation of human bromodomains was discovered to be involved in the development of a large spectrum of diseases. However, targeting eukaryotic pathogens bromodomains continues to be almost unexplored. We and others have reported the essentiality of diverse bromodomain- containing proteins in protozoa, offering a new opportunity for the development of antiparasitic drugs, especially for Trypansoma cruzi, the causative agent of Chagas’ disease. Mammalian bromodomains were classified in eight groups based on sequence similarity but parasitic bromodomains are very divergent proteins and are hard to assign them to any of these groups, suggesting that selective inhibitors can be obtained. In this review, we describe the importance of lysine acetylation and bromodomains in T. cruzi as well as the current knowledge on mammalian bromodomains. Also, we summarize the myriad of small-molecules under study to treat different pathologies and which of them have been tested in trypanosomatids and other protozoa. All the information available led us to propose that T. cruzi bromodomains should be considered as important potential targets and the search for smallmolecules to inhibit them should be empowered.

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.

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