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PubMedDate not supplied

Descriptors you can count on? Normalized and filtered pharmacophore descriptors for virtual screening.

The three-dimensional (3D) binary pharmacophore fingerprints find wide application as descriptors in applications ranging from virtual screening through library design. While the 3D content they capture is an intuitively attractive feature of such measures, maximizing their signal to noise ratio has proven to be a tricky balancing act. This issue surfaces primarily due to the potential of such fingerprints to create an explosion of pharmacophores as molecular complexity and flexibility increases. In this article, we describe a modification to the fingerprint generation process that normalizes pharmacophore occurrence frequency by the conformational ensemble size used to derive the descriptor. By including pharmacophore frequency and conformational count, the importance of a given pharmacophore is weighted by the probability of its existence within a given conformational ensemble, rather than treating each pharmacophore equally. In addition, a number of filters have been added to permit the removal of unwanted pharmacophores from the descriptor set. These filters are based on pharmacophore composition (e.g. permutations made up primarily of lipophilic and/or aromatic centers), and size (pharmacophore perimeter length relative to the largest perimeter length found in the molecule). The highly uneven nature of pharmacophore distributions across the conformational ensemble used to generate them is highlighted, as are enrichment comparisons with their binary fingerprint peers. In addition, the limitations in descriptor comparison validation are high-lighted as an illustration of the need for more extensive validation experiments.

Drug Design↗
PubMedDate not supplied

Virtual screening of novel noncovalent inhibitors for SARS-CoV 3C-like proteinase.

The SARS coronavirus 3C-like proteinase is considered as a potential drug design target for the treatment of severe acute respiratory syndrome (SARS). Owing to the lack of available drugs for the treatment of SARS, the discovery of inhibitors for SARS coronavirus 3C-like proteinase that can potentially be optimized as drugs appears to be highly desirable. We have built a "flexible" three-dimensional model for SARS 3C-like proteinase by homology modeling and multicanonical molecular dynamics method and used the model for virtual screening of chemical databases. After Dock procedures, strategies including pharmocophore model, consensus scoring, and "drug-like" filters were applied in order to accelerate the process and improve the success rate of virtual docking screening hit lists. Forty compounds were purchased and tested by HPLC and colorimetric assay against SARS 3C-like proteinase. Three of them including calmidazolium, a well-known antagonist of calmodulin, were found to inhibit the enzyme with an apparent K(i) from 61 to 178 microM. These active compounds and their binding modes provide useful information for understanding the binding sites and for further selective drug design against SARS and other coronavirus.

Binding Sites↗
PubMed1999

"Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening.

A chemically advanced template search (CATS) based on topological pharmacophore models has been developed as a technique for virtual screening. This technique has successfully identified novel potent Ca(2+) antagonists (such as 2) that have a similar activity to 1 (a known T-channel blocking agent) in a library of several hundred thousand compounds on the basis of a correlation vector representation.

Journal Article↗
PubMed2000

High-throughput and virtual screening: core lead discovery technologies move towards integration.

In addition to high-throughput screening (HTS), the main lead discovery technology employed by most pharmaceutical companies today is virtual screening (VS). Although the two techniques have somewhat different philosophical origins, they contain many synergies that can potentially enhance the lead discovery process. Here, we describe many of the latest developments in VS technology with particular emphasis on their potential impact on HTS in, for example, focussed screening and data mining. In addition, we highlight key issues that need to be addressed before the potential of such efforts can be fully realized.

Journal Article↗
PubMed2005

Focused library design in GPCR projects on the example of 5-HT(2c) agonists: comparison of structure-based virtual screening with ligand-based search methods.

The aim of this study was to investigate the usefulness of structure-based virtual screening (VS) for focused library design in G protein-coupled receptors (GPCR) projects on the example of 5-HT(2c) agonists. We compared the performance of structure-based VS against two different homology models using FRED for docking and ScreenScore, FlexX, and PMF for rescoring with the results of 12 ligand-based similarity searches using four different query compounds and three different similarity metrics (Daylight, FTree, Phacir). The result of the similarity search showed much variation, from an enrichment factor up to 3.2 to worse than random, whereas the structure-based VS gave a more stable result with a constant enrichment factor around 2. Additionally, actives retrieved by the structure-based approach were more diverse than the actives among the top scorers of the similarity searches. Based on these results, we suggest basing a focused library design for a GPCR project on a combination of a ligand-based similarity search and structure-based docking.

Cluster Analysis↗
PubMedDate not supplied

Virtual screening of 4-anilinoquinazoline analogues as EGFR kinase inhibitors: importance of hydrogen bonds in the evaluation of poses and scoring functions.

Virtual Screening (VS) is a computational technique that allows selection and ranking of possible hits from a library of compounds. We have carried out VS on 128 selected EGFR kinase inhibitors with GOLD and LigandFit. From the experimental crystal structure of the erlotinib-EGFR complex, three key hydrogen bonds were identified as responsible for anchoring the ligand in the active site. These are of the N-H...N, O(w)-H...N, and C-H...O types. Failure to include the hydrogen-bonded water molecule that forms the O(w)-H...N bond leads to incorrect results. Of the three interactions, the C-H...O formed by an activated C-H group is the best conserved. On the basis of the efficacy of these hydrogen bonds, the poses were classified into one of three categories: close, shifted, and misoriented. In the VS context, all three interactions need to be modeled correctly so that correct poses and affinities are obtained, and this happens in ligands of the close variety. Cross scoring wherein the poses from one software are input into another for scoring and consensus scoring wherein the scores from various software packages are weighted are also helpful in obtaining better agreements.

Enzyme Inhibitors↗
PubMed2005

Evaluation of library ranking efficacy in virtual screening.

We present the results of a comprehensive study in which we explored how the docking procedure affects the performance of a virtual screening approach. We used four docking engines and applied 10 scoring functions to the top-ranked docking solutions of seeded databases against six target proteins. The scores of the experimental poses were placed within the total set to assess whether the scoring function required an accurate pose to provide the appropriate rank for the seeded compounds. This method allows a direct comparison of library ranking efficacy. Our results indicate that the LigandFit/Ligscore1 and LigandFit/GOLD docking/scoring combinations, and to a lesser degree FlexX/FlexX, Glide/Ligscore1, DOCK/PMF (Tripos implementation), LigandFit1/Ligscore2 and LigandFit/PMF (Tripos implementation) were able to retrieve the highest number of actives at a 10% fraction of the database when all targets were looked upon collectively. We also show that the scoring functions rank the observed binding modes higher than the inaccurate poses provided that the experimental poses are available. This finding stresses the discriminatory ability of the scoring algorithms, when better poses are available, and suggests that the number of false positives can be lowered with conformers closer to bioactive ones.

Algorithms↗
PubMedDate not supplied

Analysis of data fusion methods in virtual screening: similarity and group fusion.

In a recent companion paper we have related the operation of simple data fusion rules used in virtual screening to a multiple integral formalism. In this paper we extend these ideas to the analysis of data fusion methods applied to real data. We examine several cases of similarity fusion using different coefficients and different representations and consider the reasons for positive or negative results in terms of the similarity distributions. Results are obtained using the SUM-, MAX- MIN-, and CombMNZ-fusion rules. We also develop a customized fusion rule, which provides an estimate of the optimal possible result for fusing multiple searches of a specific database; this shows that similarity fusion can, in principle, achieve retrieval enhancements even if this is not achieved in practice with current fusion rules. The methods are extended to analyze the comparatively successful results of group fusion with multiple actives, and we provide a rationale for the observed superiority of the MAX-rule over the SUM-rule in this context.

Combinatorial Chemistry Techniques↗
PubMed2004

Ligand-based structural hypotheses for virtual screening.

The majority of drug targets for small molecule therapeutics are proteins whose three-dimensional structure is not known to sufficient resolution to permit structure-based design. All three-dimensional QSAR approaches have a requirement for some hypothesis of ligand conformation and alignment, and predictions of molecular activity critically depend on this ligand-based binding site hypothesis. The molecular similarity function used in the Surflex docking system, coupled with quantitative pressure to minimize overall molecular volume, forms an effective objective function for generating hypotheses of bioactive conformations of sets of small molecules binding to their cognate proteins. Results are presented, assessing utility of the method for ligands of the serotonin, histamine, muscarinic, and GABA(A) receptors. The Surflex similarity module (Surflex-Sim) was able, in each case, to distinguish true ligands from random compounds using models constructed from just two or three known ligands. True positive rates of 60% were achieved with false positive rates of 0-3%; the theoretical enrichment rates were over 150-fold compared with random screening. The methods are practically applicable for rational design of ligands and for high-throughput virtual screening and offer competitive performance to many structure-based docking algorithms.

Algorithms↗
PubMed2003

Successful shape-based virtual screening: the discovery of a potent inhibitor of the type I TGFbeta receptor kinase (TbetaRI).

We describe the discovery, using shape-based virtual screening, of a potent, ATP site-directed inhibitor of the TbetaRI kinase, an important and novel drug target for fibrosis and cancer. The first detailed report of a TbetaRI kinase small molecule co-complex confirms the predicted binding interactions of our small molecule inhibitor, which stabilizes the inactive kinase conformation. Our results validate shape-based screening as a powerful tool to discover useful leads against a new drug target.

Adenosine Triphosphate↗
PubMed2003

Analysis and optimization of structure-based virtual screening protocols. 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success.

An important element of any structure-based virtual screening (SVS) technique is the method used to orient the ligands in the target active site. This has been a somewhat overlooked issue in recent SVS validation studies, with the assumption being made that the performance of an algorithm for a given set of orientation sampling settings will be representative for the general behavior of said technique. Here, we analyze five different SVS targets using a variety of sampling paradigms within the DOCK, GOLD and PROMETHEUS programs over a data set of approximately 10,000 noise compounds, combined with data sets containing multiple active compounds. These sets have been broken down by chemotype, with chemotype hit rate used to provide a measure of enrichment with a potentially improved relevance to real world SVS experiments. The variability in enrichment results produced by different sampling paradigms is illustrated, as is the utility of using pharmacophores to constrain sampling to regions that reflect known structural biology. The difference in results when comparing chemotype with compound hit rates is also highlighted.

Binding Sites↗
PubMed2005

Virtual screening for anti-HIV-1 RT and anti-HIV-1 PR inhibitors from the Thai medicinal plants database: a combined docking with neural networks approach.

The virtual screening approach for docking small molecules into a known protein structure is a powerful tool for drug design. In this work, a combined docking and neural network approach, using a self-organizing map, has been developed and applied to screen anti-HIV-1 inhibitors for two targets, HIV-1 RT and HIV-1 PR, from active compounds available in the Thai Medicinal Plants Database. Based on nevirapine and calanolide A as reference structures in the HIV-1 RT binding site and XK-263 in the HIV-1 PR binding site, 2,684 compounds in the database were docked into the target enzymes. Self-organizing maps were then generated with respect to three types of pharmacophoric groups. The map of the reference structures were then superimposed on the feature maps of all screened compounds. Only the structures having similar features to the reference compounds were accepted. By using the SOMs, the number of candidates for HIV-1 RT was reduced to six and nine compounds consistent with nevirapine and calanolide A, respectively, as references. For the HIV-1 PR target, there are 135 screened compounds showed good agreement with the XK-263 feature map. These screened compounds will be further tested for their HIV-1 inhibitory affinities. The obtained results indicate that this combined method is clearly helpful to perform the successive screening and to reduce the analyzing step from AutoDock and scoring procedure.

Anti-HIV Agents↗
PubMedDate not supplied

Assessing the discriminatory power of scoring functions for virtual screening.

The efficiency of scoring functions for hit identification is usually quantified in terms of enrichment factors and enrichment curves. Close inspection of simulated and real score distributions from virtual screening, however, suggests that 'analysis of variance' (ANOVA) is a more reliable method for assessing their performance. Using ANOVA to quantify the discriminatory power of scoring functions with respect to ligands, decoys, and a reproducible reference database has the potential to facilitate the advancement of scoring functions significantly.

Journal Article↗
PubMed2004

Evaluation and application of multiple scoring functions for a virtual screening experiment.

In order to identify novel chemical classes of factor Xa inhibitors, five scoring functions (FlexX, DOCK, GOLD, ChemScore and PMF) were engaged to evaluate the multiple docking poses generated by FlexX. The compound collection was composed of confirmed potent factor Xa inhibitors and a subset of the LeadQuest screening compound library. Except for PMF the other four scoring functions succeeded in reproducing the crystal complex (PDB code: 1FAX). During virtual screening the highest hit rate (80%) was demonstrated by FlexX at an energy cutoff of -40 kJ/mol, which is about 40-fold over random screening (2.06%). Limited results suggest that presenting more poses of a single molecule to the scoring functions could deteriorate their enrichment factors. A series of promising scaffolds with favorable binding scores was retrieved from LeadQuest. Consensus scoring by pair-wise intersection failed to enrich the hit rate yielded by single scorings (i.e. FlexX). We note that reported successes of consensus scoring in hit rate enrichment could be artificial because their comparisons were based on a selected subset of single scoring and a markedly reduced subset of double or triple scoring. The findings presented in this report are based upon a single biological system and support further studies.

Animals↗
PubMed2003

Mini-fingerprints for virtual screening: design principles and generation of novel prototypes based on information theory.

Binary fingerprint representations of molecular structure and properties are convenient computational tools for similarity searching in compound databases and virtual screening (VS). We are investigating the design of relatively simple fingerprints for the identification of molecules having similar biological activity and recognition of remote similarity relationships. Since our designs are considerably shorter than other fingerprints used in VS, we have previously termed them "mini-fingerprints" (MFPs). A key aspect of the design strategy is the identification of suitable molecular descriptors. Whereas our initial fingerprint designs have relied on descriptor combinations that performed well in compound classification according to biological activity, second generation MFPs encode combinations of descriptors with high information content in large compound databases and high frequency of occurrence in drug-like molecules. Thus, the design of these new fingerprints does not depend on the analysis of specific classes of bioactive compounds, but rather on descriptor information content in large compound databases. Systematic evaluation of fingerprint performance in VS test calculations demonstrates that these new prototypes perform better than previously generated MFPs. The analysis described herein provides an example for the development of search tools for VS.

Environmental Pollutants↗
PubMedDate not supplied

Virtual screening using binary kernel discrimination: analysis of pesticide data.

This paper discusses the use of binary kernel discrimination (BKD) for identifying potential active compounds in lead-discovery programs. BKD was compared with established virtual screening methods in a series of experiments using pesticide data from the Syngenta corporate database. It was found to be superior to methods based on similarity searching and substructural analysis but inferior to a support vector machine. Similar conclusions resulted from application of the methods to a pesticide data set for which categorical activity data were available.

Algorithms↗
PubMed2006

Identification of ellagic acid as potent inhibitor of protein kinase CK2: a successful example of a virtual screening application.

Casein kinase 2 (CK2) is a ubiquitous, essential, and highly pleiotropic protein kinase whose abnormally high constitutive activity is suspected to underlie its pathogenic potential in neoplasia and other diseases. Using a virtual screening approach, we have identified the ellagic acid, a naturally occurring tannic acid derivative, as a novel potent CK2 inhibitor. At present, ellagic acid represents the most potent known CK2 inhibitor (K(i) = 20 nM).

Binding Sites↗
PubMed2004

Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors.

A method of easily finding ligands, with a variety of core structures, for a given target macromolecule would greatly contribute to the rapid identification of novel lead compounds for drug development. We have developed an efficient method for discovering ligand candidates from a number of flexible compounds included in databases, when the three-dimensional (3D) structure of the drug target is available. The method, named ADAM&EVE, makes use of our automated docking method ADAM, which has already been reported. Like ADAM, ADAM&EVE takes account of the flexibility of each molecule in databases, by exploring the conformational space fully and continuously. Database screening has been made much faster than with ADAM through the tuning of parameters, so that computational screening of several hundred thousand compounds is possible in a practical time. Promising ligand candidates can be selected according to various criteria based on the docking results and characteristics of compounds. Furthermore, we have developed a new tool, EVE-MAKE, for automatically preparing the additional compound data necessary for flexible docking calculation, prior to 3D database screening. Among several successful cases of lead discovery by ADAM&EVE, the finding of novel acetylcholinesterase (AChE) inhibitors is presented here. We performed a virtual screening of about 160 000 commercially available compounds against the X-ray crystallographic structure of AChE. Among 114 compounds that could be purchased and assayed, 35 molecules with various core structures showed inhibitory activities with IC(50) values less than 100 microM. Thirteen compounds had IC(50) values between 0.5 and 10 microM, and almost all their core structures are very different from those of known inhibitors. The results demonstrate the effectiveness and validity of the ADAM&EVE approach and provide a starting point for development of novel drugs to treat Alzheimer's disease.

Acetylcholinesterase↗