scholarly journals Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

2012 ◽  
Vol 9 (77) ◽  
pp. 3196-3207 ◽  
Author(s):  
Pedro J. Ballester ◽  
Martina Mangold ◽  
Nigel I. Howard ◽  
Richard L. Marchese Robinson ◽  
Chris Abell ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput Screening ◽  
Streptomyces Coelicolor ◽  
Screening Tools ◽  
Molecular Scaffolds ◽  
Efficient Manner ◽  
Modern Drug ◽  
Screening Protocol ◽  
Molecular Databases ◽  
Hit Identification

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor ), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated K i ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.

Virtual screening: a real screening complement to high-throughput screening

2002 ◽  
Vol 30 (4) ◽  
pp. 797-799 ◽  
Author(s):  
J. Mestres
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Screening ◽  
Oestrogen Receptor ◽  
Essential Elements ◽  
The Other ◽  
Receptor Subtype ◽  
Other Hand ◽  
Hit Identification

Virtual screening is being routinely used as an integral part of today's hit-identification strategies for, on one hand, prioritizing large corporate screening collections and, on the other hand, to extend the scope of screening to external databases. A brief description of the essential elements required for virtual screening and an application example to the identification of agonist hits for the oestrogen receptor subtype ERα are presented.

scholarly journals Performance Studies on Distributed Virtual Screening

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jens Krüger ◽  
Richard Grunzke ◽  
Sonja Herres-Pawlis ◽  
Alexander Hoffmann ◽  
Luis de la Garza ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Performance Studies ◽  
High Throughput Screening ◽  
Drug Target ◽  
Science Gateway ◽  
Chemical Structures ◽  
Modern Drug ◽  
Benchmark Datasets ◽  
Concurrent Processes ◽  
Distributed Computing Infrastructures

Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly.

Virtual Screening Methods Based on Bayesian Statistics

2011 ◽  
pp. 190-211 ◽  
Author(s):  
Martin Vogt ◽  
Jürgen Bajorath
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Characteristic Feature ◽  
Bayesian Statistics ◽  
High Throughput Screening ◽  
Screening Methods ◽  
Compound Libraries ◽  
Computational Screening ◽  
Starting Point ◽  
Hit Identification

Computational screening of in silico-formatted compound libraries, often termed virtual screening (VS), has become a standard approach in early-phase drug discovery. In analogy to experimental high-throughput screening (HTS), VS is mostly applied for hit identification, although other applications such as database filtering are also pursued. Contemporary VS approaches utilize target structure and/or ligand information as a starting point. A characteristic feature of current ligand-based VS approaches is that many of these methods differ substantially in the complexity of the underlying algorithms and also of the molecular representations that are utilized. In recent years, probabilistic VS methods have become increasingly popular in the field and are currently among the most widely used ligand-based approaches. In this contribution, the authors will introduce and discuss selected methodologies that are based on Bayesian principles.

scholarly journals High-Throughput Screening—Based Identification of Paramyxovirus Inhibitors

2008 ◽  
Vol 13 (7) ◽  
pp. 591-608 ◽  
Author(s):  
Jeong-Joong Yoon ◽  
Dhruv Chawla ◽  
Tanja Paal ◽  
Maina Ndungu ◽  
Yuhong Du ◽  
...  
Keyword(s):  
High Throughput ◽  
Viral Entry ◽  
High Throughput Screening ◽  
Measles Virus ◽  
Human Pathogens ◽  
Response Curves ◽  
Major Morbidity ◽  
Screening Protocol ◽  
Hit Identification ◽  
Chemistry Journal

Several members of the paramyxovirus family constitute major human pathogens that, collectively, are responsible for major morbidity and mortality worldwide. In an effort to develop novel therapeutics against measles virus (MV), a prominent member of the paramyxovirus family, the authors report a high-throughput screening protocol that uses a nonrecombinant primary MV strain as targets. Implementation of the assay has yielded 60 hit candidates from a 137,500-entry library. Counterscreening and generation of dose-response curves narrows this pool to 35 compounds with active concentrations ≤15.3 µM against the MV-Alaska strain and specificity indices ranging from 36 to >500. Library mining for structural analogs of several confirmed hits combined with retesting of identified candidates reveals a high accuracy of primary hit identification. Eleven of the confirmed hits interfere with viral entry, whereas the remaining 24 compounds target postentry steps of the viral life cycle. Activity testing against selected members of the paramyxovirus family reveals 3 patterns of activity: 1) exclusively MV-specific blockers, 2) inhibitors of MV and related viruses of the same genus, and 3) broader range inhibitors with activity against a different Paramyxovirinae genus. Representatives of the last class may open avenues for the development of broad-range paramyxovirus inhibitors through hit-to-lead chemistry. ( Journal of Biomolecular Screening 2008:591-608)

VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening

2020 ◽  
Vol 27 (38) ◽  
pp. 6480-6494 ◽  
Author(s):  
José-Manuel Gally ◽  
Stéphane Bourg ◽  
Jade Fogha ◽  
Quoc-Tuan Do ◽  
Samia Aci-Sèche ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
In Silico ◽  
Three Dimensional ◽  
Bioactive Molecules ◽  
Screening Tools ◽  
Computational Time ◽  
In Silico Drug Design ◽  
Molecular Databases ◽  
Design Projects

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.

Discovery of New AKT1 Inhibitors by Combination of In silico Structure Based Virtual Screening Approaches and Biological Evaluations

2019 ◽  
Author(s):  
Filip Fratev ◽  
Denisse A. Gutierrez ◽  
Renato J. Aguilera ◽  
suman sirimulla
Keyword(s):  
Virtual Screening ◽  
Success Rate ◽  
Protein Interactions ◽  
In Silico ◽  
Biological Data ◽  
In Vitro Binding ◽  
Vitro Binding ◽  
Screening Protocol ◽  
Screening Approaches

AKT1 is emerging as a useful target for treating cancer. Herein, we discovered a new set of ligands that inhibit the AKT1, as shown by in vitro binding and cell line studies, using a newly designed virtual screening protocol that combines structure-based pharmacophore and docking screens. Taking together with the biological data, the combination of structure based pharamcophore and docking methods demonstrated reasonable success rate in identifying new inhibitors (60-70%) proving the success of aforementioned approach. A detail analysis of the ligand-protein interactions was performed explaining observed activities.<br>

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 High-throughput computational-experimental screening protocol for the discovery of bimetallic catalysts

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Byung Chul Yeo ◽  
Hyunji Nam ◽  
Hyobin Nam ◽  
Min-Cheol Kim ◽  
Hong Woo Lee ◽  
...  
Keyword(s):  
High Throughput ◽  
Bimetallic Catalysts ◽  
High Throughput Screening ◽  
Direct Synthesis ◽  
Catalytic Performance ◽  
Platinum Group Metals ◽  
Pt Catalyst ◽  
Electronic Density ◽  
Bimetallic Alloy ◽  
Screening Protocol

AbstractTo accelerate the discovery of materials through computations and experiments, a well-established protocol closely bridging these methods is required. We introduce a high-throughput screening protocol for the discovery of bimetallic catalysts that replace palladium (Pd), where the similarities in the electronic density of states patterns were employed as a screening descriptor. Using first-principles calculations, we screened 4350 bimetallic alloy structures and proposed eight candidates expected to have catalytic performance comparable to that of Pd. Our experiments demonstrate that four bimetallic catalysts indeed exhibit catalytic properties comparable to those of Pd. Moreover, we discover a bimetallic (Ni-Pt) catalyst that has not yet been reported for H2O2 direct synthesis. In particular, Ni61Pt39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides an opportunity for the catalyst discovery for the replacement or reduction in the use of the platinum-group metals.

scholarly journals Action plan for hit identification (APHID): KAT6A as a case study

2020 ◽  
Vol 12 (5) ◽  
pp. 423-437 ◽  
Author(s):  
Xiangyan Yi ◽  
Lian Xue ◽  
Tim Thomas ◽  
Jonathan B Baell
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Action Plan ◽  
Hit Identification

Here, we describe our action plan for hit identification (APHID) that guides the process of hit triage, with elimination of less tractable hits and retention of more tractable hits. We exemplify the process with reference to our high-throughput screening (HTS) campaign against the enzyme, KAT6A, that resulted in successful identification of a tractable hit. We hope that APHID could serve as a useful, concise and digestible guide for those involved in HTS and hit triage, especially those that are relatively new to this exciting and continually evolving technology.

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