Molecular Architect: A User-Friendly Workflow for Virtual Screening (original) (raw)
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Ligand-Based Virtual Screening Approach Using a New Scoring Function
Journal of Chemical Information and Modeling, 2012
In this study, we aimed to develop a new ligand-based virtual screening approach using an effective shape-overlapping procedure and a more robust scoring function (denoted by the HWZ score for convenience). The HWZ score-based virtual screening approach was tested against the compounds for 40 protein targets available in the Database of Useful Decoys (DUD) (dud.docking.org/jahn/), and the virtual screening performance was evaluated in terms of the area under the ROC curve (AUC), Enrichment Factor (EF), and Hit Rate (HR), demonstrating an improved overall performance compared to other popularly used approaches examined. In particular, the HWZ score-based virtual screening led to an average AUC value of 0.84 ± 0.02 (95% confidence interval) for the 40 targets. The average HR values at top 1% and 10% of the active compounds for the 40 targets were 46.3% ± 6.7% and 59.2% ± 4.7%, respectively. In addition, the performance of the HWZ score-based virtual screening approach is less sensitive to the choice of the target.
Building a virtual ligand screening pipeline using free software: a survey
Briefings in Bioinformatics, 2015
Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.
The Use of Consensus Scoring in Ligand-Based Virtual Screening
Journal of Chemical Information and Modeling, 2006
A new consensus approach has been developed for ligand-based virtual screening. It involves combining highly disparate properties in order to improve performance in virtual screening. The properties include structural, 2D pharmacophore and property-based fingerprints, scores derived using BCUT descriptors, and 3D pharmacophore approaches. Different approaches for the combination of all or some of these methods have been tested. Logistic regression and sum ranks were found to be the most advantageous in different pharmaceutical applications. The three major reasons consensus scoring appears to enrich data sets better than single scoring functions are (1) using multiple scoring functions is similar to repeated samplings, in which case the mean is closer to the true value than any single value, (2) due to the better clustering of actives, multiple sampling will recover more actives than inactives, and (3) different methods seem to agree more on the ranking of the actives than on the inactives. Furthermore, consensus results are not only better but are also more consistent across receptor systems.
Journal of Computer-Aided Molecular Design, 2007
Four different ligand-based virtual screening scenarios are studied: (1) prioritizing compounds for subsequent high-throughput screening (HTS); (2) selecting a predefined (small) number of potentially active compounds from a large chemical database; (3) assessing the probability that a given structure will exhibit a given activity; (4) selecting the most active structure(s) for a biological assay. Each of the four scenarios is exemplified by performing retrospective ligand-based virtual screening for eight different biological targets using two large databases-MDDR and WOMBAT. A comparison between the chemical spaces covered by these two databases is presented. The performance of two techniques for ligand-based virtual screening-similarity search with subsequent data fusion (SSDF) and novelty detection with Self-Organizing Maps (ndSOM) is investigated. Three different structure representations-2,048dimensional Daylight fingerprints, topological autocorrelation weighted by atomic physicochemical properties (sigma electronegativity, polarizability, partial charge, and identity) and radial distribution functions weighted by the same atomic physicochemical properties-are compared. Both methods were found applicable in scenario one. The similarity search was found to perform slightly better in scenario two while the SOM novelty detection is preferred in scenario three. No method/descriptor combination achieved significant success in scenario four.
Virtual screening strategies in drug discovery: a critical review
Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points for medicinal chemistry. The number of methods and softwares which use the ligand and target-based VS approaches is increasing at a rapid pace. What, however, are the real advantages and disadvantages of the VS technology and how applicable is it to drug discovery projects? This review provides a comprehensive appraisal of several VS approaches currently available. In the first part of this work, an overview of the recent progress and advances in both ligand-based VS (LBVS) and structurebased VS (SBVS) strategies highlighting current problems and limitations will be provided. Special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology, the knowledge of which would help medicinal chemists to design more potent clinical candidates with fewer side effects. In the second part, recent case studies (all published in the last two years) will be discussed where the VS technology has been applied successfully. A critical analysis of these case studies provides a good platform in order to estimate the applicability of various VS strategies in the new lead identification and optimization.
Journal of Medicinal Chemistry, 2000
Three different database docking programs (Dock, FlexX, Gold) have been used in combination with seven scoring functions (Chemscore, Dock, FlexX, Fresno, Gold, Pmf, Score) to assess the accuracy of virtual screening methods against two protein targets (thymidine kinase, estrogen receptor) of known three-dimensional structure. For both targets, it was generally possible to discriminate about 7 out of 10 true hits from a random database of 990 ligands. The use of consensus lists common to two or three scoring functions clearly enhances hit rates among the top 5% scorers from 10% (single scoring) to 25-40% (double scoring) and up to 65-70% (triple scoring). However, in all tested cases, no clear relationships could be found between docking and ranking accuracies. Moreover, predicting the absolute binding free energy of true hits was not possible whatever docking accuracy was achieved and scoring function used. As the best docking/consensus scoring combination varies with the selected target and the physicochemistry of target-ligand interactions, we propose a two-step protocol for screening large databases: (i) screening of a reduced dataset containing a few known ligands for deriving the optimal docking/consensus scoring scheme, (ii) applying the latter parameters to the screening of the entire database.
Overview of Different Software Used in Drug Design and Drug Discovery: A Review
2010
The new drug discovery is found to be a time consuming and costly process. Recently, a trend towards the use of in-silico computational chemistry and molecular modeling for computer aided drug design has gained significant momentum. This review is an investigation of the applications of different software in drug design. The modern drug discovery process is steadily becoming more information driven. Structural, physicochemical and ADME information about property profiles of reference ligands, along with structural information of their target proteins, have been extremely useful for early-stage drug discovery. A number of examples have recently been reported for the successful applications of structure based drug design to the discovery of compounds with a potential to become more useful therapeutic agents. Among the reviewed software programs are applications programmed in Grid computing, window based general PBPK/PD modeling software, PKUDDS for structure based drug design, APIS, JAVA, Perl and Python, CADD as well as software including software libraries. These all programs are useful for cheminformatics approaches to drug design and discovery including QSAR studies, energy minimization as well as docking studies in drug design. Furthermore this review explains options for using different computer modeling software programs in drug discovery and design.
Strategies of Virtual Screening in Medicinal Chemistry
International Journal of Quantitative Structure-Property Relationships, 2018
Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.