Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “virtual screening”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources
PubMed2006

Current status of virtual screening as analysed by target class.

In silico virtual screening for drug discovery has become a hot topic in medicinal chemistry research during the last 5 years, growing from a largely academic pursuit concerned principally with validating the methods used, to a major early-stage technique for lead discovery in the pharmaceutical industry. In this review we highlight a few recent successes in ligand docking associated with virtual screening, paying particular attention to four major target classes of pharmaceutical interest (G Protein-Coupled receptors, nuclear hormone receptors, kinases, proteases). We also discuss some emerging trends in the field, some common limitations, and how they are being overcome.

Binding Sites↗
PubMedDate not supplied

A novel search engine for virtual screening of very large databases.

Virtual screening of large chemical databases using the structure of the receptor can be computationally very demanding. We present a novel strategy that combines exhaustive similarity searches directly in SMILES format with the docking of flexible ligands, whose 3D structure is generated on the fly from the SMILES representation. Our strategy makes use of the recently developed LINGO tools to extract implicit chemical information from SMILES strings and integrates LINGO similarities into a pseudo-evolutionary algorithm. The algorithm represents a combination of a fast target-independent similarity method with a slower but information richer target-focused method. A virtual search of FactorXa ligands provided 62% of the potential hits after docking only 6.5% of a database of nearly 1 million molecules. The set of solutions showed good diversity, indicating that the method shows good scaffold hopping capabilities.

Algorithms↗
PubMed2005

Statistical tools for virtual screening.

In large-scale virtual screening (VS) campaigns, data are often computed for millions of compounds to identify leads, but there remains the task of prioritizing VS "hits" for experimental assays and the dilemma of assessing true/false positives. We present two statistical methods for mining large databases: (1) a general scoring metric based on the VS signal-to-noise level within a compound neighborhood; (2) a neighborhood-based sampling strategy for reducing database size, in lieu of property-based filters.

Algorithms↗
PubMed2002

Structure-based virtual screening: an overview.

Enormous advances in genomics have resulted in a large increase in the number of potential therapeutic targets that are available for investigation. This growth in potential targets has increased the demand for reliable target validation, as well as technologies that can identify rapidly several quality lead candidates. Virtual screening, and in particular receptor-based virtual screening, has emerged as a reliable, inexpensive method for identifying leads. Although still an evolving method, advances in computational techniques have enabled virtual screening to have a positive impact on the discovery process. Here, the current strengths and weaknesses of the technology are discussed, and emphasis is placed on aspects of the work-flow of a virtual screening campaign, from preparation through to post-screening analysis.

Animals↗
PubMed2006

Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring.

The ability to find novel bioactive scaffolds in compound similarity-based virtual screening experiments has been studied comparing Tanimoto-based, ranking-based, voting, and consensus scoring protocols. Ligand sets for seven well-known drug targets (CDK2, COX2, estrogen receptor, neuraminidase, HIV-1 protease, p38 MAP kinase, thrombin) have been assembled such that each ligand represents its own unique chemotype, thus ensuring that each similarity recognition event between ligands constitutes a scaffold hopping event. In a series of virtual screening studies involving 9969 MDDR compounds as negative controls it has been found that atom pair descriptors and 3D pharmacophore fingerprints combined with ranking, voting, and consensus scoring strategies perform well in finding novel bioactive scaffolds. In addition, often superior performance has been observed for similarity-based virtual screening compared to structure-based methods. This finding suggests that information about a target obtained from known bioactive ligands is as valuable as knowledge of the target structures for identifying novel bioactive scaffolds through virtual screening.

Cyclin-Dependent Kinase 2↗
PubMed2004

Virtual screening for kinase targets.

Kinases have become a major area of drug discovery and structure-based design. Hundreds of 3D structures for more than thirty different kinases are available to the public. High structural and sequence homology within the kinase gene family makes the remaining kinases ideal targets for homology modeling and virtual screening. Somewhat surprisingly, however, the number of publications about virtual screening of kinases is very low. Therefore, rather than reviewing the field of virtual screening for kinases, we attempt here a hybrid approach of presenting what is known and common practice together with new studies on CDK2 and SRC kinase. To illustrate the challenges and pitfalls of virtual screening for kinase targets we focus on the question of how ranking is influenced by the database screened, the docking scheme, the scoring function, the activity of the compounds used for testing, and small changes in the binding pocket. In addition, a case study of finding irreversible inhibitors of ErbB2 through in silico screening is presented.

Binding Sites↗
PubMed2004

FlexX-Scan: fast, structure-based virtual screening.

We present a new software module, FlexX-Scan, for high-throughput, structure-based virtual screening. FlexX-Scan was developed with the aim to further speed up the virtual screening process. Based on the incremental construction docking tool FlexX (Rarey et al., J Mol Biol 1996;261:470-489), a compact descriptor for representing favorable protein interaction spots within the protein binding site has been developed. The descriptor is calculated using special-purpose clustering techniques applied to the usual interaction points created by FlexX. The algorithm automatically detects a small set of interaction spots in the binding site for positioning ligand functional groups. The parametrizations of the base placement and incremental construction algorithms have been adapted to the new interaction model. We tested the software tool on a diverse set of 200 protein-ligand complexes from the protein database (PDB) (Kramer et al., Proteins 1999;37:228-241). On average, the algorithm proposes about 90 interaction spots per binding site compared to about 1000 interaction dots in FlexX. We observe that the docking solutions of FlexX-Scan have a root-mean-square deviation from the crystal structure similar to the deviation of docking solutions of standard FlexX. For further validation we also performed virtual screening experiments for cyclin-dependent kinase 2, thrombin, angiotensin-converting enzyme, and dihydrofolat reductase. In these experiments, we screened a set of 34,000 random compounds and a number of known actives for each target. With FlexX-Scan, we achieved comparable enrichments to standard FlexX, with an averaged computing time of 5-10 s per compound, depending on parametrization.

Algorithms↗
PubMed2005

Identification of phosphodiesterase-1 and 5 dual inhibitors by a ligand-based virtual screening optimized for lead evolution.

We identified new lead candidates which showed potent dual inhibition against phosphodiesterase-1 and 5 by a ligand-based virtual screening optimized for lead evolution. This virtual screening method, consisting of classification and regression tree analysis using 168 2-center pharmacophore descriptors and 12 macroscopic descriptors, demonstrated a high predictive ability for bioactivity of new chemical compounds. The obtained lead candidates were structurally diverse, although only the structure-activity relationship data of hydroxamic acid derivatives were used to configure the prediction model for the virtual screening.

Drug Evaluation, Preclinical↗
PubMed2005

Multiple-ligand-based virtual screening: methods and applications of the MTree approach.

We present a novel approach for ligand-based virtual screening by combining query molecules into a multiple feature tree model called MTree. All molecules are described by the established feature tree descriptor, which is derived from a topological molecular graph. A new pairwise alignment algorithm leads to a consistent topological molecular alignment based on chemically reasonable matching of corresponding functional groups. These multiple feature tree models find application in ligand-based virtual screening to identify new lead structures for chemical optimization. Retrospective virtual screening with MTree models generated for angiotensin-converting enzyme and the alpha1a receptor on a large candidate database yielded enrichment factors up to 71 for the first 1% of the screened database. MTree models outperformed database searches using single feature trees in terms of hit rates and quality and additionally identified alternative molecular scaffolds not included in any of the query molecules. Furthermore, relevant molecular features, which are known to be important for affinity to the target, are identified by this new methodology.

Adrenergic alpha-1 Receptor Antagonists↗
PubMed2005

Virtual screening for beta-secretase (BACE1) inhibitors reveals the importance of protonation states at Asp32 and Asp228.

A comparative virtual screen for beta-secretase (BACE1) inhibitors using different docking methods (FlexX and FlexX-Pharm), scoring functions (Dock, Gold, Chem, PMF, FlexX), protonation states (default and calculated), and protein conformations (apo and ligand bound) has been performed. Apo and ligand bound conformations of BACE1 were both found to be suitable for virtual screening. Assigning calculated protonation states to catalytic Asp32 and Asp228 residues resulted in significant improvement of enrichment factors as calculated at 1% of the ranked database. Using 1FKN we obtained no enrichment by FlexX/D-Score that was improved to 36 when considering calculated protonation states. We also show that combining calculated protonation states with pharmacophore constraints using FlexX-Pharm/D-Score improved enrichment further to 41. Enrichments reported in this study suggest our screening protocol will be effective in the virtual screening of large compound libraries for BACE1 inhibitors.

Amyloid Precursor Protein Secretases↗
PubMedDate not supplied

Novel 2D fingerprints for ligand-based virtual screening.

This paper describes the development of a set of new 2D fingerprints for the purposes of virtual screening in a pharmaceutical environment. The new fingerprints are based on established ones: the changes in their design included the introduction of overlapping pharmacophore feature types, feature counts for pharmacophore and structural fingerprints, as well as changes in the resolution in property description for property fingerprints. The effects of each of these changes on virtual screening performance were monitored using two types of training sets, emulating different stages in the drug discovery process. The results demonstrate that these changes all lead to an improvement in virtual screening performance.

Chemistry↗
PubMed2007

Discovering potassium channel blockers from synthetic compound database by using structure-based virtual screening in conjunction with electrophysiological assay.

Potassium ion (K+) channels are attractive targets for drug discovery because of the essential roles played in biological systems. However, high-throughput screening (HTS) cannot be used to screen K+ channel blockers. To overcome this disadvantage of HTS, we have developed a virtual screening approach for discovering novel blockers of K+ channels. On the basis of a three-dimensional model of the eukaryotic K+ channels, molecular docking-based virtual screening was employed to search the chemical database MDL Available Chemicals Directory (ACD). Compounds were ranked according to their relative binding energy, favorable shape complementarity, and potential to form hydrogen bonds with the outer mouth of the K+ channel model. Twenty candidate compounds selected from the virtual screening were examined using the whole-cell voltage-clamp recording in rat dissociated hippocampal neurons. Among them, six compounds (5, 6, 8, 18-20) potently blocked both the delayed rectifier (IK) and fast transient K+ currents (IA). When applied externally, these six compounds preferentially blocked IK with potencies 2- to 500-fold higher than that of tetraethylammonium chloride. Intracellular application of the six compounds had no effect on both K+ currents. In addition, the interaction models and binding free energy calculations demonstrated that hydrophobic interaction and solvent effects play important roles in the inhibitory activities of these compounds. The results demonstrated that structure-based computer screening strategy could be used to identify novel, structurally diverse compounds targeting the pore binding pocket of the outer mouth of voltage-gated K+ channels. This study provides an alternative way of finding new blockers of voltage-gated K+ channels, while the techniques for high-throughput screening of K+ channel drugs remain in development.

Animals↗
PubMed2006

Electrophysiological characterization of 14-benzoyltalatisamine, a selective blocker of the delayed rectifier K+ channel found in virtual screening.

14-Benzoyltalatisamine is a potent and selective blocker of the delayed rectifier K+ channel found in a computational virtual screening study. The compound was found to block the K+ channel from the extracellular side. However, it is unclear whether 14-benzoyltalatisamine shares the same block mechanism with tetraethylammonium (TEA). In order to elucidate how the hit compound found by the virtual screening interacts with the outer vestibule of the K+ channel, the effects of 14-benzoyltalatisamine and TEA on the delayed rectifier K+ current of rat dissociated hippocampal neurons were compared using whole-cell voltage-clamp recording. External application of 14-benzoyltalatisamine and TEA reversibly inhibited the current with IC50 values of 10.1+/-2.2 microM and 1.05+/-0.21 mM, respectively. 14-Benzoyltalatisamine exerted voltage-dependent inhibition, markedly accelerated the decay of the current, and caused a significant hyperpolarizing shift of the steady-state activation curve, whereas TEA caused voltage-independent inhibition, without affecting the kinetic parameters of the current. The blockade by 14-benzoyltalatisamine, but not by TEA, was significantly diminished in a high K+ (60 mM) external solution. The potency of 14-benzoyltalatisamine was markedly reduced in the presence of 15 mM TEA. The results suggest that 14-benzoyltalatisamine bind to the external pore entry of the delayed rectifier K+ channel with partial insertion into the selectivity filter, which is in conformity with that predicted by the molecular docking model in the virtual screening.

Aconitine↗
PubMedDate not supplied

ZINC--a free database of commercially available compounds for virtual screening.

A critical barrier to entry into structure-based virtual screening is the lack of a suitable, easy to access database of purchasable compounds. We have therefore prepared a library of 727,842 molecules, each with 3D structure, using catalogs of compounds from vendors (the size of this library continues to grow). The molecules have been assigned biologically relevant protonation states and are annotated with properties such as molecular weight, calculated LogP, and number of rotatable bonds. Each molecule in the library contains vendor and purchasing information and is ready for docking using a number of popular docking programs. Within certain limits, the molecules are prepared in multiple protonation states and multiple tautomeric forms. In one format, multiple conformations are available for the molecules. This database is available for free download (http://zinc.docking.org) in several common file formats including SMILES, mol2, 3D SDF, and DOCK flexibase format. A Web-based query tool incorporating a molecular drawing interface enables the database to be searched and browsed and subsets to be created. Users can process their own molecules by uploading them to a server. Our hope is that this database will bring virtual screening libraries to a wide community of structural biologists and medicinal chemists.

Databases, Factual↗
PubMed2004

Structure-based virtual screening and biological evaluation of potent and selective ADAM12 inhibitors.

We describe a series of potent and selective inhibitors of ADAM12 that were discovered using computational screening of a focused virtual library. The initial structure-based virtual screening selected 64 compounds from a 3D database of 67,062 molecules. Being evaluated by a cell-based ADAM12 activity assay, compounds 5, 11, 14, 16 were further identified as the potent and selective inhibitors of ADAM12 with low nanomolar IC50 values. The mechanism underlying the potency and selectivity of a representative compound, 5, was investigated through molecular docking studies.

ADAM Proteins↗
PubMedDate not supplied

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theoretical predictions was observed. Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the molecular discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.

Algorithms↗
PubMedDate not supplied

The use of consensus scoring in ligand-based virtual screening.

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 Article↗
PubMedDate not supplied

Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example.

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.

Computer Simulation↗