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At least 73 records · Page 4Linked to original sources
PubMed2006

Application of linear discriminant analysis in the virtual screening of antichagasic drugs through trypanothione reductase inhibition.

We have performed virtual screening to identify new lead trypanothione reductase inhibitor (TRI) compounds, enzyme present in Tripanozoma cruzi, the agent responsible of Chagas disease. From a training set of 58 compounds, linear discriminant analysis (LDA) was performed using 2D and 3D descriptors as discriminating variables in order to find out which function of descriptors characterizes the active TRI. The values of the statistical parameters F--Snedecor and Wilk's lambda for the discriminant function (DF) showed good statistical significance, as long as the rate of success in the prediction for both the training and the test set: 91.38% and 88.63%, in that order. Internal validation through the Leave--Group--Out methodology was performed with good results, assuring the stability of the DF. Afterwards, the DF was applied in virtual screening of 422,367 compounds. The optimum range of values of octanol--water partition coefficient for a compound to develop trypanothione reductase inhibition was applied as a second filtering criteria. 739 structurally heterogeneous drugs of the virtual library were selected as promissory TRI.

Animals↗
PubMed2005

Extraction and visualization of potential pharmacophore points using support vector machines: application to ligand-based virtual screening for COX-2 inhibitors.

Support vector machines (SVM) were trained to predict cyclooxygenase 2 (COX-2) and thrombin inhibitors. The classifiers were obtained using sets of known COX-2 and thrombin inhibitors as "positive examples" and a large collection of screening compounds as "negative examples". Molecules were encoded by topological pharmacophore-point triangles. In retrospective virtual screening, 50-90% of the known active compounds were listed within the first 0.1% of the ranked database. To check the validity of the constructed classifiers, we developed a method for feature extraction and visualization using SVM. As a result, potential pharmacophore points were weighted according to their importance for COX-2 and thrombin inhibition. Known thrombin and COX-2 pharmacophore points were correctly recognized by the machine learning system. In a prospective virtual screening study, several potential COX-2 inhibitors were predicted and tested in a cellular activity assay. A benzimidazole derivative exhibited significant inhibitory activity with an IC(50) of 0.2 microM, which is better than Celecoxib in our assay. It was demonstrated that the SVM machine-learning method can be used in virtual screening and be analyzed in a human-interpretable way that results in a set of rules for designing novel molecules.

Binding Sites↗
PubMed2006

Structure selection for protein kinase docking and virtual screening: homology models or crystal structures?

There is currently far more sequence information than structural information available, and the ability to use homology models for virtual screening applications is desirable in many cases where structures have not yet been solved. This review focuses on the application of protein kinase homology models for virtual screening use. In addition to reviewing previous cases in which kinase homology models have been used in inhibitor design, we present new data - useful for template selection in homology modeling applications - indicating that the template structure with the highest sequence or structural similarity with the target structure may not always be the best choice. This new work explored the simple hypothesis that better results might be obtained for docking a ligand to a target receptor using a homology model of the target created from a different kinase template co-crystallized with the ligand, than from a crystal structure of the actual kinase target that is unliganded or bound to an unrelated ligand. This hypothesis was tested in docking studies of staurosporine with eight different kinases: AutoDock was used to dock staurosporine to homology models of each kinase created from staurosporine-bound template structures, and the results were compared with docking staurosporine to crystal structures of the target kinase that were obtained in complex with a non-staurosporine ligand or no ligand. It was found that the homology models performed as well as or better than the crystal structures, suggesting that using a homology model created from a template crystallized with a representative ligand may in some cases be a preferred approach, especially in virtual screening experiments that focus on enriching for members of a particular inhibitor class.

Crystallography, X-Ray↗
PubMed2005

Novel and potent NPY5 receptor antagonists derived from virtual screening and iterative parallel chemistry design.

In the quest for NPY5 receptor antagonists a virtual screening approach yielded a novel and potent hit class from a limited compound selection. The tight and seamless integration between virtual screening and rapid parallel chemistry within the framework of the Roche Lead Generation unit led in only two rounds of iterative chemistry optimisation to a much broader understanding of the factors which influence the potency of the thiazole hit class.

Animals↗
PubMed2003

Topological virtual screening: a way to find new anticonvulsant drugs from chemical diversity.

A topological virtual screening (tvs) test is presented, which is capable of identifying new drug leaders with anticonvulsant activity. Molecular structures of both anticonvulsant-active and non active compounds, extracted from the Merck Index database, were represented using topological indexes. By means of the application of a linear discriminant analysis to both sets of structures, a topological anticonvulsant model (tam) was obtained, which defines a connectivity function. On the basis of this model, 41 new structures with anticonvulsant activity have been identified by a topological virtual screening.

Anticonvulsants↗
PubMed2003

Protein-based virtual screening of chemical databases. II. Are homology models of G-Protein Coupled Receptors suitable targets?

The aim of the current study is to investigate whether homology models of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 A-resolution X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound" form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three-dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge- and pharmacophore-based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist-bound and agonist-bound states).

Adrenergic beta-2 Receptor Agonists↗
PubMedDate not supplied

Conditional probability: a new fusion method for merging disparate virtual screening results.

This paper introduces a new consensus scoring approach for merging the results of different virtual screening methods based on conditional probabilities. The technique is experimentally evaluated using several ligand-based virtual screening methods and compared to two variations of the established Sum-rank fusion method where it performs as well or better than the Sum-rank methods. Our experiments confirm that consensus scoring increases the number of active compounds retrieved with respect to the best individual methods on average.

Journal Article↗
PubMedDate not supplied

Virtual screen for ligands of orphan G protein-coupled receptors.

This paper describes a virtual screening methodology that generates a ranked list of high-binding small molecule ligands for orphan G protein-coupled receptors (oGPCRs), circumventing the requirement for receptor three-dimensional structure determination. Features representing the receptor are based only on physicochemical properties of primary amino acid sequence, and ligand features use the two-dimensional atomic connection topology and atomic properties. An experimental screen comprised nearly 2 million hypothetical oGPCR-ligand complexes, from which it was observed that the top 1.96% predicted affinity scores corresponded to "highly active" ligands against orphan receptors. Results representing predicted high-scoring novel ligands for many oGPCRs are presented here. Validation of the method was carried out in several ways: (1) A random permutation of the structure-activity relationship of the training data was carried out; by comparing test statistic values of the randomized and nonshuffled data, we conclude that the value obtained with nonshuffled data is unlikely to have been encountered by chance. (2) Biological activities linked to the compounds with high cross-target binding affinity were analyzed using computed log-odds from a structure-based program. This information was correlated with literature citations where GPCR-related pathways or processes were linked to the bioactivity in question. (3) Anecdotal, out-of-sample predictions for nicotinic targets and known ligands were performed, with good accuracy in the low-to-high "active" binding range. (4) An out-of-sample consistency check using the commercial antipsychotic drug olanzapine produced "active" to "highly-active" predicted affinities for all oGPCRs in our study, an observation that is consistent with documented findings of cross-target affinity of this compound for many different GPCRs. It is suggested that this virtual screening approach may be used in support of the functional characterization of oGPCRs by identifying potential cognate ligands. Ultimately, this approach may have implications for pharmaceutical therapies to modulate the activity of faulty or disease-related cellular signaling pathways. In addition to application to cell surface receptors, this approach is a generalized strategy for discovery of small molecules that may bind intracellular enzymes and involve protein-protein interactions.

Drug Evaluation, Preclinical↗
PubMed2003

Virtual screening on natural products for discovering active compounds and target information.

Natural products, containing inherently large-scale structural diversity than synthetic compounds, have been the major resources of bioactive agents and will continually play as protagonists for discovering new drugs. However, how to access this diverse chemical space efficiently and effectively is an exciting challenge for medicinal chemists and pharmacologists. While virtual screening, which has shown a great promise in drug discovery, will play an important role in digging out lead (active) compounds from natural products. This review focuses on the strategy of virtual screening based on molecular docking and, with successful examples from our laboratory, illustrates the efficiency of virtual screening in discovering active compounds from natural products. On the other hand, the sequencing of the human genome and numerous pathogen genomes has resulted in an unprecedented opportunity for discovering potential new drug targets. Chemogenomics has appeared as a new technology to initiate target discovery by using active compounds as probes to characterize proteome functions. Natural products are the ideal probes for such research. Binding affinity fingerprint is a powerful chemogenomic descriptor to characterize both small molecules and pharmacologically relevant proteins. Therefore, this review also discusses binding affinity fingerprint strategy for identifying target information from the genomic data by using natural products as the probes.

Biological Products↗
PubMed2004

Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D).

A novel method (MOLPRINT 3D) for virtual screening and the elucidation of ligand-receptor binding patterns is introduced that is based on environments of molecular surface points. The descriptor uses points relative to the molecular coordinates, thus it is translationally and rotationally invariant. Due to its local nature, conformational variations cause only minor changes in the descriptor. If surface point environments are combined with the Tanimoto coefficient and applied to virtual screening, they achieve retrieval rates comparable to that of two-dimensional (2D) fingerprints. The identification of active structures with minimal 2D similarity ("scaffold hopping") is facilitated. In combination with information-gain-based feature selection and a naive Bayesian classifier, information from multiple molecules can be combined and classification performance can be improved. Selected features are consistent with experimentally determined binding patterns. Examples are given for angiotensin-converting enzyme inhibitors, 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors, and thromboxane A2 antagonists.

Angiotensin-Converting Enzyme Inhibitors↗
PubMedDate not supplied

Mapping algorithms for molecular similarity analysis and ligand-based virtual screening: design of DynaMAD and comparison with MAD and DMC.

Here, we introduce the DynaMAD algorithm that is designed to map database compounds to combinations of activity-class-dependent descriptor value ranges in order to identify novel active molecules. The method combines and extends key features of two previously developed algorithms, MAD and DMC. These methods were first described as compound-mapping algorithms for large-scale virtual screening applications. DynaMAD and DMC operate in chemical spaces of stepwise increasing dimensionality. However, in contrast to DMC, which utilizes binary transformed descriptors, DynaMAD uses unmodified descriptor value distributions. The performance of these mapping methods was compared in detail in virtual screening trials on 24 different compound activity classes against a background of about 2 million database compounds. In these calculations, all three approaches produced results of considerable predictive value, and the enrichment of active molecules in small selection sets consisting of only about 20 or fewer database compounds emerged as a common feature. Furthermore, mapping methods were capable of recognizing remote molecular similarity relationships. Overall, DynaMAD performed better than MAD and DMC, producing average hit and recovery rates of 55% and 33%, respectively, over all 24 classes. Taken together, our findings suggest that dynamic compound mapping to combinations of activity-class-selective descriptor settings has significant potential for molecular similarity analysis and ligand-based virtual screening.

Algorithms↗
PubMed2005

Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4.

The "receiver operating characteristic" (ROC) curve method is a well-recognized metric used as an objective way to evaluate the ability of a given test to discriminate between two populations. This facilitates decision-making in a plethora of fields in which a wrong judgment may have serious consequences including clinical diagnosis, public safety, travel security, and economic strategies. When virtual screening is used to speed-up the drug discovery process in pharmaceutical research, taking the right decision upon selecting or discarding a molecule prior to in vitro evaluation is of paramount importance. Characterizing both the ability of a virtual screening workflow to select active molecules and the ability to discard inactive ones, the ROC curve approach is well suited for this critical decision gate. As a case study, the first virtual screening workflow focused on metabotropic glutamate receptor subtype 4 (mGlu4R) agonists is reported here. Six compounds out of 38 selected and tested in vitro were shown to have agonist activity on this target of therapeutic interest.

Binding Sites↗
PubMed2003

Analysis and optimization of structure-based virtual screening protocols (1): exploration of ligand conformational sampling techniques.

Ligand conformational flexibility has long been recognized as an important issue in virtual screening (VS). To this end, a number of different methodologies have been adapted to tackle the problem. Many of said techniques were originally designed for ligand derived pharmacophore screens, but have subsequently been fashioned for application within structure-based virtual screening (SVS). A popular adaptation is the pre-calculation of diverse ligand conformations for subsequent docking in target active sites. In this paper, we study a number of the software programs currently being used in conformer generation, analyzing their ability to regenerate known ligand binding conformations. The implications of these studies are discussed, from the perspective of VS in general and SVS in particular.

Computational Biology↗
PubMed2006

High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening.

In order to assess bioactivity profiles for small organic molecules we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual molecules. In this proof of principle study, carried out with the new structure-based pharmacophore modelling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their known biological targets could be correctly predicted via an enrichment of corresponding pharmaco-phores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approximately 90% of all input molecules. Additionally, we discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biological activities of a compound or a compound library by screening it against a series of pharmacophore queries.

Antiviral Agents↗
PubMed2002

Structure-based approaches to drug design and virtual screening.

Structure-based design has made an important contribution to drug discovery for many years. Recently, the increasing availability of structural data and the affordability of high-performance computing platforms have broadened the applicability of these methods. In particular, virtual screening has been adopted as an effective paradigm for lead discovery that fits in well alongside high-throughput screening programs. Structure-based virtual screening relies on fast and accurate computational methods for the prediction of receptor-ligand binding modes and binding affinities. In this paper, recent technical advances in the field of molecular docking and de novo design are reviewed, in particular, the development of flexible receptor models, docking of combinatorial libraries and novel scoring methods.

Animals↗
PubMed2005

A linear discrimination analysis based virtual screening of trichomonacidal lead-like compounds: outcomes of in silico studies supported by experimental results.

A computational (virtual) screening test to identify potential trichomonacidals has been developed. Molecular structures of trichomonacidal and non-trichomonacidal drugs were represented using stochastic and non-stochastic atom-based quadratic indices and a linear discrimination analysis (LDA) was trained to classify molecules regarding their antiprotozoan activity. Validation tests revealed that our LDA-QSAR models recognize at least 88.24% of trichomonacidal lead-like compounds and suggest using this methodology in virtual screening protocols. These classification functions were then applied to find new lead antitrichomonal compounds. In this connection, the biological assays of eight compounds, selected by computational screening using the present models, give good results (87.50% of good classification). In general, most of the compounds showed high activity against Trichomonas vaginalis at the concentration of 100 microg/ml and low cytotoxicity to this concentration. In particular, two heterocyclic derivatives (VA7-67 and VA7-69) maintained their efficacy at 10 microg/ml with an important trichomonacidal activity (100.00% of reduction), but it is remarkable that the compound VA7-67 did not show cytotoxic effects in macrophage cultivations. 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.

Animals↗
PubMed2006

3D pharmacophore based virtual screening of T-type calcium channel blockers.

Virtual screening of the commercial databases was done by using a three dimensional pharmacophore previously developed for T-type calcium channel blockers using CATALYSTtrade mark program. Biological evaluation of 25 selected virtual hits resulted in the discovery of a highly potent compound VH04 with IC(50) value of 0.10 microM, eight times as potent as the known selective T-type calcium channel blocker, mibefradil. Search for similar compounds yielded several hits with micro-molar IC(50) values and high T-type calcium channel selectivity. Based on the structure of the virtual hits, small molecule libraries with novel scaffolds were designed, synthesis and biological evaluation of which are currently in progress. This result shows a successful example of ligand based drug discovery of potent T-type calcium channel blockers.

Animals↗