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Results for “virtual screening”

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At least 523 records · Page 29Linked to original sources
PubMed1999

The SHAPES strategy: an NMR-based approach for lead generation in drug discovery.

BACKGROUND: Recently, it has been shown that nuclear magnetic resonance (NMR) may be used to identify ligands that bind to low molecular weight protein drug targets. Recognizing the utility of NMR as a very sensitive method for detecting binding, we have focused on developing alternative approaches that are applicable to larger molecular weight drug targets and do not require isotopic labeling. RESULTS: A new method for lead generation (SHAPES) is described that uses NMR to detect binding of a limited but diverse library of small molecules to a potential drug target. The compound scaffolds are derived from shapes most commonly found in known therapeutic agents. NMR detection of low (microM-mM) affinity binding is achieved using either differential line broadening or transferred NOE (nuclear Overhauser effect) NMR techniques. CONCLUSIONS: The SHAPES method for lead generation by NMR is useful for identifying potential lead classes of drugs early in a drug design program, and is easily integrated with other discovery tools such as virtual screening, high-throughput screening and combinatorial chemistry.

IMP Dehydrogenase↗
PubMedDate not supplied

Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints: simultaneous optimization and structure-based diversity.

New applications of fingerprints of multiple potential 4-point three-dimensional (3D) pharmacophores in combinatorial library design and virtual screening are presented. Preliminary results demonstrating the feasibility of a simulated annealing process for combinatorial reagent selection that concurrently optimizes product diversity in BCUT chemistry space and in terms of unique 4-point pharmacophores are discussed, and the advantage of using a customized chemistry-space derived for the library design is demonstrated. In addition, an extension to the multiple pharmacophore method for structure-based design that uses the shape of the target site as an additional constraint is presented. This development enables the docking process to be quantified in terms of the number and identities of the pharmacophoric hypotheses that can be matched by a compound or a library of compounds. The design of an example combinatorial library based on the Ugi condensation reaction and a serine protease active site is described.

Binding Sites↗
PubMed2002

The potential of Internet computing for drug discovery.

Large-scale, high precision drug discovery calculations, such as predicting protein folding or small-molecule protein inhibitors, have frustrated computational chemists because the supercomputers currently available are insufficiently powerful. The increasing power of PCs offers an alternative approach by harnessing 'idle time' from corporate and home computers that are connected to the Internet or an intranet. However, although the approach has the potential of offering hundreds or thousands of years of computer time per elapsed day, the architecture constraints require computational chemists to choose their methods and applications with care. Some algorithms, such as those for molecular simulations, are generally not appropriate, whereas virtual screening of molecules for protein inhibition works well.

Algorithms↗
PubMed2002

Bio- and chemo-informatics beyond data management: crucial challenges and future opportunities.

Bio- and chemo-informatics are now thought to be crucial to the success and integration of biotechnology and drug discovery. Research in this area has expanded to go beyond data- and information-management. Here, we review exemplary areas, such as target identification and validation, virtual screening, and prediction of downstream characteristics of leads, where further research will play a key role in progressing the field.

Biotechnology↗
PubMed2002

Structural biology in drug design: selective protein kinase inhibitors.

Protein kinases have a fundamental role in signal transduction pathways, and aberrant kinase activity has been observed in many diseases. In recent years, kinase inhibition has become a major area for therapeutic intervention and a variety of kinase inhibitor pharmacophores has been described. This review illustrates some of the efforts and results in the field of structure-based design of protein kinase inhibitors. The methods and results discussed here illustrate the power of structure-based design in lead discovery, for example via virtual screening and in guiding the optimization of the pharmacological properties of these molecules.

Adenosine Triphosphate↗
PubMed2002

Grid technologies empowering drug discovery.

Grid technologies enable flexible coupling and sharing of computers, instruments and storage. Grids can provide technical solutions to the volume of data and computational demands associated with drug discovery by delivering larger computing capability (flexible resource sharing), providing coordinated access to large data resources and enabling novel online exploration (coupling computing, data and instruments online). Here, we illustrate this potential by describing two applications: the use of desktop PC grid technologies for virtual screening, and distributed X-ray structure reconstruction and online visualization.

Computational Biology↗
PubMed2003

ADME-Tox in drug discovery: integration of experimental and computational technologies.

Over the past ten years, in vitro experimental tools to characterize ADME-Tox profiles of compounds have been applied in early stages of the drug discovery process to increase the success rate of discovery programmes and to progress better candidates into drug development. Application of in silico ADME-Tox models has further enhanced discovery support, enabling virtual screening of compounds and thus, application of ADME-Tox at every stage of the discovery process. Ultimately, effective and efficient ADME-Tox support of discovery will depend on a complementary and synergistic use of experimental and in silico ADME-Tox.

Animals↗
PubMed2004

Detailed kinetic studies of an aggregating inhibitor; inhibition of phosphomannomutase/phosphoglucomutase by disperse blue 56.

Phosphomannomutase/phosphoglucomutase occupies a central position in the pathways by which several virulence factors are synthesized in Pseudomonas aeruginosa. Virtual screening was used to identify potential inhibitors of phosphomannomutase/ phosphoglucomutase, and one compound, the anthraquinone-based dye Disperse Blue 56, showed potent inhibition in vitro. The kinetics of inhibition was complex; the time courses for reactions in the presence of the inhibitor were biphasic, suggestive of slow-binding inhibition. Quantitative analysis of the progress curves and preincubation experiments demonstrated that slow-binding inhibition was not occurring, however. Initial velocity kinetic studies indicated that Disperse Blue 56 was a parabolic, noncompetitve inhibitor. Progress curves for reactions in the presence of Disperse Blue 56 could be fitted very well by a model in which 2 equiv of the inhibitor bound to free enzyme or the enzyme-substrate complex. The inhibition was largely relieved by the inclusion of 0.01% Triton X-100 in the assay solutions, which has been suggested to be the hallmark for inhibition by compounds that exert their effect through aggregates [McGovern, S. L., Caselli, E., Grigorieff, N., and Shiochet, B. K. (2002) J. Med. Chem. 45, 1712-1722]. Our kinetic data appear to be consistent with either inhibition by a dimer of Disperse Blue 56 or inhibition by a Disperse Blue 56 aggregate, but the latter appears much more likely. We present a detailed analysis of the system to provide further information that may help in the recognition of inhibition through aggregation.

Anthraquinones↗
PubMed2006

Inhibitor discovery targeting the intermediate structure of beta-amyloid peptide on the conformational transition pathway: implications in the aggregation mechanism of beta-amyloid peptide.

Abeta peptides cleaved from the amyloid precursor protein are the main components of senile plaques in Alzheimer's disease. Abeta peptides adopt a conformation mixture of random coil, beta-sheet, and alpha-helix in solution, which makes it difficult to design inhibitors based on the 3D structures of Abeta peptides. By targeting the C-terminal beta-sheet region of an Abeta intermediate structure extracted from molecular dynamics simulations of Abeta conformational transition, a new inhibitor that abolishes Abeta fibrillation was discovered using virtual screening in conjunction with thioflavin T fluorescence assay and atomic force microscopy determination. Circular dichroism spectroscopy demonstrated that the binding of the inhibitor increased the beta-sheet content of Abeta peptides either by stabilizing the C-terminal beta-sheet conformation or by inducing the intermolecular beta-sheet formation. It was proposed that the inhibitor prevented fibrillation by blocking interstrand hydrogen bond formation of the pleated beta-sheet structure commonly found in amyloid fibrils. The study not only provided a strategy for inhibitor design based on the flexible structures of amyloid peptides but also revealed some clues to understanding the molecular events involved in Abeta aggregation.

Amyloid↗
PubMedDate not supplied

Focused combinatorial library design based on structural diversity, druglikeness and binding affinity score.

The advent of focused library and virtual screening has reduced the disadvantage of combinatorial chemistry and changed it to a realizable and cost-effective tool in drug discovery. Usually, genetic algorithms (GAs) are used to quickly finding high-scoring molecules by sampling a small subset of the total combinatorial space. Therefore, scoring functions play essential roles in focused library design. Reported here is our initial attempt to establish a new approach for generating a target-focused library using the combination of the scores of structural diversity and binding affinity with our newly improved drug-likeness scoring functions. Meanwhile, a software package, named LD1.0, was developed on the basis of the new approach. One test on a cyclooxygenase (COX)2-focused library successfully reproduced the structures that have been experimentally studied as COX2-selective inhibitors. Another test is on a peroxisome proliferator-activated receptors gamma-focused library design, which not only reproduces the key fragments in the approved (thiazolidinedione) TZD drugs, but also generates some new structures that are more active than the approved drugs or published ligands. Both of the two tests took approximately 15% of the running time of the ordinary molecular docking method. Thus, our new approach is an effective, reliable, and practical way for building up a properly sized focused library with a high hit rate, novel structure, and good ADME/T profile.

Algorithms↗
PubMedDate not supplied

QSAR comparative study of Wiener descriptors for weighted molecular graphs.

Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies use statistical models to compute physical, chemical, or biological properties of a chemical substance from its molecular structure, encoded in a numerical form with the aid of various descriptors. Structural indices derived from molecular graph matrices represent an important group of descriptors used in QSPR and QSAR models; recently, their utilization was extended to molecular similarity and diversity, in database mining and virtual screening of combinatorial libraries. Initially defined from the distance matrix, the Wiener index W was the source of novel graph descriptors derived from recently proposed molecular matrices and of the Wiener graph operator. In this work we present a comparative study of several Wiener-type descriptors computed for vertex- and edge-weighted molecular graphs, corresponding to organic compounds with heteroatoms and multiple bonds. The acute toxicities toward Tetrahymena pyriformis of 47 nitrobenzenes are modeled with multilinear regression equations, using as structural descriptors the hydrophobicity (corrected for ionization) and various Wiener-type indices, with better results than a comparative molecular field analysis model.

Journal Article↗
PubMedDate not supplied

Mini-fingerprints detect similar activity of receptor ligands previously recognized only by three-dimensional pharmacophore-based methods.

Mini-fingerprints (MFPs) are short binary bit string representations of molecular structure and properties, composed of few selected two-dimensional (2D) descriptors and a number of structural keys. MFPs were specifically designed to recognize compounds with similar activity. Here we report that MFPs are capable of detecting similar activities of some druglike molecules, including endothelin A antagonists and alpha(1)-adrenergic receptor ligands, the recognition of which was previously thought to depend on the use of multiple point three-dimensional (3D) pharmacophore methods. Thus, in these cases, MFPs and pharmacophore fingerprints produce similar results, although they define, in terms of their complexity, opposite ends of the spectrum of methods currently used to study molecular similarity or diversity. For each of the studied compound classes, comparison of MFP bit settings identified a consensus or signature pattern. Scaling factors can be applied to these bits in order to increase the probability of finding compounds with similar activity by virtual screening.

Angiotensin II↗
PubMedDate not supplied

CLIP: similarity searching of 3D databases using clique detection.

This paper describes a program for 3D similarity searching, called CLIP (for Candidate Ligand Identification Program), that uses the Bron-Kerbosch clique detection algorithm to find those structures in a file that have large structures in common with a target structure. Structures are characterized by the geometric arrangement of pharmacophore points and the similarity between two structures calculated using modifications of the Simpson and Tanimoto association coefficients. This modification takes into account the fact that a distance tolerance is required to ensure that pairs of interatomic distances can be regarded as equivalent during the clique-construction stage of the matching algorithm. Experiments with HIV assay data demonstrate the effectiveness and the efficiency of this approach to virtual screening.

Journal Article↗
PubMedDate not supplied

Design of virtual libraries of umami-tasting molecules.

The introduction of molecular tools in food research offers the possibility to the food industry to benefit from the experience gained in the field by pharmaceutical companies. In this work we are showing how in silico virtual screening techniques based on molecular similarity were applied for identifying novel umami-tasting compounds. The results obtained suggest that 5'-ribonucleotides and monosodium glutamate might elicit the fifth basic taste via the same molecular mechanism. New algorithms were developed and used in this work, such as the dimension reduction of data sets by singular value decomposition and the introduction of the correlation dimension as a natural dimension of a chemical space. It is shown that the representations of molecular data sets in chemical spaces possess self-similar properties, characteristic of fractal objects.

Algorithms↗
PubMedDate not supplied

Chemometrical classification of ephrin ligands and Eph kinases using GRID/CPCA approach.

Eph receptor tyrosine kinases are divided on two subfamilies based on their affinity for ephrin ligands and play a crucial role in the intercellular processes such as angiogenesis, neurogenesis, and carcinogenesis. As such, Eph kinases represent potential targets for drug design, which requires the knowledge of structural features responsible for their specific interactions. To overcome the existing gap between available sequence and structure information we have built 3D models of eight ephrins and 13 Eph kinase ligand-binding domains using homology modeling techniques. The interaction energies for several molecular probes with binding sites of these models were calculated using GRID and subjected to chemometrical classification based on consensus principal component analysis (CPCA). Despite inherent limitations of the homology models, CPCA was able to successfully distinguish between ephrins and Eph kinases, between Eph kinase subfamilies, and between ephrin subfamilies. As a result we have identified several amino acids that may account for selectivity in ephrin-Eph kinase interactions. In general, although the difference in charge between ephrin and Eph kinase binding domains creates an attractive long-range electrostatic force, the hydrophobic and steric interactions are highly important for the short-range interactions between two proteins. The chemometrical analysis also provides the pharmacophore model, which could be used for virtual screening and de novo ligand design.

Amino Acid Sequence↗
PubMedDate not supplied

TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides.

A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in herbicides using computer-aided molecular design. Two series of compounds, one containing herbicide and the other containing nonherbicide compounds, were processed by a k-Means Cluster Analysis in order to design the training and prediction sets. A linear classification function to discriminate the herbicides from the nonherbicide compounds was developed. The model correctly and clearly classified 88% of active and 94% of inactive compounds in the training set. More specifically, the model showed a good global classification of 91%, i.e., (168 cases out of 185). While in the prediction set, they showed an overall predictability of 91% and 92% for active and inactive compounds, being the global percentage of good classification of 92%. To assess the range of model applicability, a virtual screening of structurally heterogeneous series of herbicidal compounds was carried out. Two hundred eighty-four out of 332 were correctly classified (86%). Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments toward herbicidal property; also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general "in silico" technique to experimentation in herbicides discovery.

Cluster Analysis↗
PubMedDate not supplied

Blood-brain barrier permeation models: discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates.

The aim of this article is to present the design of a large heterogeneous CNS library (approximately 1700 compounds) from WDI and mapping CNS drugs using QSAR models of blood-brain barrier (BBB) permeation and P-gp substrates. The CNS library finally includes 1336 BBB-crossing drugs (BBB+), 259 molecules non-BBB-crossing (BBB-), and 91 P-gp substrates (either BBB+ or BBB-). Discriminant analysis and PLS-DA have been used to model the passive diffusion component of BBB permeation and potential physicochemical requirement of P-gp substrates. Three categories of explanatory variables (Cdiff, BBBpred, PGPpred) have been suggested to express the level of permeation within a continuous scale, starting from two classes data (BBB+/BBB-), allowing that the degree to which each compound belongs to an activity class is given using a membership score. Finally, statistical data analyses have shown that some very simple descriptors are sufficient to evaluate BBB permeation in most cases, with a high rate of well-classified drugs. Moreover, a "CNS drugs" map, including P-gp substrates and accurately reflecting the in vivo behavior of drugs, is proposed as a tool for CNS drug virtual screening.

ATP Binding Cassette Transporter, Subfamily B↗
PubMedDate not supplied

SVM-based feature selection for characterization of focused compound collections.

Artificial neural networks, the support vector machine (SVM), and other machine learning methods for the classification of molecules are often considered as a "black box", since the molecular features that are most relevant for a given classifier are usually not presented in a human-interpretable form. We report on an SVM-based algorithm for the selection of relevant molecular features from a trained classifier that might be important for an understanding of ligand-receptor interactions. The original SVM approach was extended to allow for feature selection. The method was applied to characterize focused libraries of enzyme inhibitors. A comparison with classical Kolmogorov-Smirnov (KS)-based feature selection was performed. In most of the applications the SVM method showed sustained classification accuracy, thereby relying on a smaller number of molecular features than KS-based classifiers. In one case both methods produced comparable results. Limiting the calculation of descriptors to only the most relevant ones for a certain biological activity can also be used to speed up high-throughput virtual screening.

Algorithms↗