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Results for “virtual screening”

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At least 487 records · Page 27Linked to original sources
PubMed2004

The pharmacophore hypotheses of I(Kr) potassium channel blockers: novel class III antiarrhythmic agents.

Predictive pharmacophore models were developed for a large series of I(Kr) potassium channel blockers as class III antiarrhythmic agents using HypoGen in Catalyst software. The pharmacophore hypotheses were generated using a training set consisting of 34 compounds carefully selected from documents. Their biological data, expressed as IC(50), spanned from 1.5 nM to 2.8 mM with 7 orders difference. The most predictive hypothesis (Hypo1), consisting of four features (one positive ionizable feature, two aromatic rings and one hydrophobic group), had a best correlation coefficient of 0.825, a lowest rms deviation of 1.612, and a highest cost difference (null cost-total cost) of 77.552, which represents a true correlation and a good predictivity. The hypothesis Hypo1 was then validated by a test set consisting of 21 compounds and by a cross-validation of 95% confidence level with randomizing the data using CatScramble program. Accordingly, our model has strong predictivity to identify structural diverse I(Kr) potassium channel blockers with desired biological activity by virtual screening

Anti-Arrhythmia Agents↗
PubMed2005

Pharmacophore identification of alpha(1A)-adrenoceptor antagonists.

A chemical feature based pharmacophore model was developed for alpha(1A)-adrenoceptor antagonists by HypoGen module implemented in catalyst software package. The best scoring pharmacophore hypothesis, Hypo1, consisted of four important chemical features (one positive ion, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group). The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.

Adrenergic alpha-1 Receptor Antagonists↗
PubMed2005

In silico fragment-based discovery of indolin-2-one analogues as potent DNA gyrase inhibitors.

We describe here the fragment-based design of potent DNA gyrase inhibitors. Using the tools of virtual screening and NMR spectroscopy we identified the binding of two low-molecular weight fragments (2-aminobenzimidazole and indolin-2-one) to the 24kDa N-terminal fragment of DNA gyrase B. Further in silico optimization of indolin-2-one led to the discovery of potent DNA gyrase inhibitors.

Computer Simulation↗
PubMed2005

Structure-based discovery of a new class of Hsp90 inhibitors.

Docking-based virtual screening identified 1-(2-phenol)-2-naphthol compounds as a new class of Hsp90 inhibitors of low to sub-micromolar potency. Here we report the binding affinities and cellular activities of several members of this class. A high resolution crystal structure of the most potent compound reveals its binding mode in the ATP binding site of Hsp90, providing a rationale for the observed activity of the series and suggesting strategies for developing compounds with improved properties.

HSP90 Heat-Shock Proteins↗
PubMed2005

In silico design and synthesis of piperazine-1-pyrrolidine-2,5-dione scaffold-based novel malic enzyme inhibitors.

Fragment-based virtual library design and virtual screening have been conducted against malic enzyme (ME) homology model. Several scaffolds have been identified as promising motifs to target ME's NADP binding site. One small focused library has been synthesized and tested against ME. Several compounds from this library have shown sub-micromolar inhibitory activity against malic enzyme.

Binding Sites↗
PubMed2006

In silico fragment-based discovery of DPP-IV S1 pocket binders.

Dipeptidyl peptidase IV is a clinically validated target for type-2 diabetes and belongs to a family of peptidases with a quite unique post-proline cleavage specificity. Known inhibitors contain a limited number of molecular anchors occupying the small prototypical S1 pocket. A virtual screening approach for such S1-binding fragments was carried out using FlexX docking to evaluate its potential to confirm known and find novel compounds. Several low molecular weight inhibitors exhibiting activities in the micromolar range could be identified as starting points for structure-based design.

Binding Sites↗
PubMed2006

Identification and optimisation of a series of substituted 5-(1H-pyrazol-3-yl)-thiophene-2-hydroxamic acids as potent histone deacetylase (HDAC) inhibitors.

Optimisation of ADS100380, a sub-micromolar HDAC inhibitor identified using a virtual screening approach, led to a series of substituted 5-(1H-pyrazol-3-yl)-thiophene-2-hydroxamic acids (6a-i), that possessed significant HDAC inhibitory activity. Subsequent functionalisation of the pendent phenyl group of compounds 6f and 6g provided analogues 6j-w with further enhanced enzyme and anti-proliferative activity. Compound 6j demonstrated efficacy in a mouse xenograft experiment.

Animals↗
PubMed2006

Pharmacophore identification of KSP inhibitors.

A three-dimensional pharmacophore model was developed based on 25 currently available KSP (kinesin spindle protein) inhibitors in Catalyst software package. The best pharmacophore hypothesis (Hypo1), consisting of four chemical features (one hydrogen-bond acceptor, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group), has a correlation coefficient of 0.965. The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.

Artificial Intelligence↗
PubMed2004

Simple stochastic fingerprints towards mathematical modelling in biology and medicine. 1. The treatment of coccidiosis.

We have developed a classification function that is capable of discriminating between anticoccidial and nonanticoccidial compounds with different structural patterns. For this purpose, we calculated the Markovian electron delocalization negentropies of several compounds. These molecular descriptors, which act as molecular fingerprints, are derived from an electronegativity-weighted stochastic matrix (1Pi). The method attempts to describe the delocalization of electrons with time during the process of molecule formation by considering the 3D environment of the atoms. Accordingly, the entropies of this random process are used as molecular descriptors. The present study involves a stochastic generalization of the original idea described by Kier, which concerned the use of molecular negentropies in QSAR. Linear discriminant analysis allowed us to fit the discriminant function. This function has given rise to a good classification of 82.35% (28 anticoccidials out of 34) and 91.8% of inactive compounds (56/61) in training series. An overall classification of 88.42% (84/95) was achieved. Validation of the model was carried out by means of an external predicting series and this gave a global predictability of 93.1%. Finally, we report the experimental assay (more than 95% of lesion control) of two compounds selected from a large data set through virtual screening. We conclude that the approach described here seems to be a promising 3D-QSAR tool based on the mathematical theory of stochastic processes.

Animals↗
PubMed2004

Development of KiBank, a database supporting structure-based drug design.

KiBank is a database of inhibition constant (Ki) values with 3D structures of target proteins and chemicals. Ki values were accumulated from peer-reviewed literature searched via PubMed. The 3D structure files of target proteins were originally from Protein Data Bank (PDB), while the 2D structure files of the chemicals were collected together with the Ki values and then converted into 3D ones. In KiBank, the chemical and protein 3D structures with hydrogen atoms were optimized by energy minimization and stored in MDL MOL and PDB format, respectively. KiBank is designed to support structure-based drug design. It provides structure files of proteins and chemicals ready for use in virtual screening through automated docking methods, while the Ki values can be applied for tests of docking/scoring combinations, program parameter settings, and calibration of empirical scoring functions. Additionally, the chemical structures and corresponding Ki values in KiBank are useful for lead optimization based on quantitative structure-activity relationship (QSAR) techniques. KiBank is updated on a daily basis and is freely available at . As of August 2004, KiBank contains 8000 Ki values, over 6000 chemicals and 166 proteins covering the subtypes of receptors and enzymes.

Binding Sites↗
PubMed2005

Assessment of chemical libraries for their druggability.

High throughput virtual screening is acknowledged as the initial means for identifying hit compounds that will be eventually transformed to leads or drug candidates. To improve quality of screening, it is essential to have powerful methods for the analysis of the compound databases. For this purpose, we have developed a novel and practical scoring function to assess the druggability of compounds. The proposed function consists of 12 metrics that take into account physical, chemical and structural properties as well as the presence of undesirable functional groups. We have applied this 12-metric scoring function to 44 different databases that include more than 3.8 million compounds, which are commercially available. The overall quality of each database was evaluated according to the score and rank measured by our 12-metric function. Our findings suggest that, the majority of compounds that do not satisfy druggable rules do so due to high molecular weight, high logP values and the presence of reactive functional groups.

Chemistry, Pharmaceutical↗
PubMed2006

A causal relationship discovery-based approach to identifying active components of herbal medicine.

Herbal medicine is widely applied for clinical use in East Asia and other countries. However, unclear correlation between its complex chemical composition and bioactivity prevents its application in the West. In the present study, a stepwise causal adjacent relationship discovery algorithm has been developed to study correlation between composition and bioactivity of herbal medicine and identify active components from the complex mixture. This approach was successfully applied in discovering active constituents from mixed extracts of Radix Salviae miltiorrhizae and Cortex Moutan. Moreover, advantage of the present approach compared with bioassay-guided isolation was demonstrated by its application on a typical herbal drug. The current work offers a new way to virtually screen active components of herbal medicine, and it might be helpful to accelerate the process of new drug discovery from natural products.

Algorithms↗
PubMed2006

Integrating molecular design resources within modern drug discovery research: the Roche experience.

Various computational disciplines, such as cheminformatics, ADME modeling, virtual screening, chemogenomics search strategies and classic structure-based design, should be seen as one multifaceted discipline contributing to the early drug discovery process. Although significant resources enabling these activities have been established, their true integration into daily research should not be taken for granted. This article reviews value-adding activities from target assessment to lead optimization, and highlights the technical and process-related aspects that can be considered essential for performance and alignment within the research organization.

Animals↗
PubMed2006

Consensus scoring for protein-ligand interactions.

This article reviews the application of consensus scoring for cases when the target 3D structure is known. Comparing the performance of different methods is not a trivial task, and it appears that consensus scoring usually substantially improves virtual screening performance, contributing to better enrichments. It also seems to improve--albeit less dramatically--the prediction of bound conformations and poses. The prediction of binding energies is still rather inaccurate and although consensus scoring generally improves these predictions, more development is required before it can be used for this purpose in routine lead optimization.

Binding Sites↗
PubMed2005

Generation of predictive pharmacophore model for SARS-coronavirus main proteinase.

Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery. In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARS-CoV). Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugs containing the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. This means that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for further SARS-CoV main proteinase inhibitor development.

Antiviral Agents↗
PubMed2006

Structure-based pharmacophore of COX-2 selective inhibitors and identification of original lead compounds from 3D database searching method.

A four-point pharmacophore of COX-2 selective inhibitors was derived from a training set of 16 compounds, using the Catalyst program. It consists of a H bond acceptor, two hydrophobic groups and an aromatic ring, in accordance with SAR data of the compounds and with topology of the COX-2 active site. This hypothesis, combined with exclusion volume spheres representing important residues of the COX-2 binding site, was used to virtually screen the Maybridge database. Eight compounds were selected for an in vitro enzymatic assay. Five of them show COX-2 inhibition close to that of nimesulide and rofecoxib, two reference COX-2 selective inhibitors. As a result, structure-based pharmacophore generation was able to identify original lead compounds, inhibiting the COX-2 isoform.

Binding Sites↗
PubMed2005

Multivariate modeling of cytochrome P450 3A4 inhibition.

In the early phases of current pharmaceutical research projects, huge numbers of compounds are tested on their biological activity with respect to a certain target by experimental or virtual screening campaigns. To reduce the attrition rate in later stages of a project, other relevant properties such as physicochemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) properties should be assessed as early as possible in lead discovery and optimization. The present study describes the development of in silico models to predict the inhibition of human cytochrome P450 3A4 (CYP3A4) from calculated molecular descriptors. The models were trained and validated using a set of 967 structural diverse drug-like research compounds with an experimentally determined CYP3A4 inhibition potency (IC50 value) which was carefully split into a training and a test set. For classification models, the data sets were further subdivided into strong, medium, and weak inhibitors. Different descriptor sets were used to cover various aspects of molecular properties, including properties derived from the 2D structure, the interaction of the molecule with its environment, and properties derived from quantum-mechanical calculations. The descriptors were related to the CYP3A4 inhibition potency by multivariate data analysis methods such as partial least-squares projection to latent structures (PLS), PLS discriminant analysis (PLS-DA), and soft independent class modeling (SIMCA). The squared correlation between experimental and predicted IC50 values of the previously unseen test set compounds was Qext2=0.6 for the best PLS models, corresponding to a root mean squared error (RMSE) of RMSE=0.45 (logarithm of IC50). The best PLS-DA models were able to correctly classify more than 60% of the test set compounds, whereas almost no strong inhibitors were wrongly classified as weak inhibitors and vice versa. Furthermore, relevant molecular properties were identified which are closely related to the CYP3A4 inhibition potency of a compound. The results presented here are very encouraging since our models could, for instance, serve to flag problematic compounds or to guide further synthesis efforts.

Cytochrome P-450 CYP3A↗
PubMed2026

FusionTarget: Computational framework for drug repurposing against modeled fusion protein structures from genomic breakpoints.

Many fusion genes have been recognized as biomarkers and therapeutic targets. However, the lack of knowledge on protein structures and targeting approaches made it challenging to develop effective targeting therapeutics. To fill this, we developed a computational pipeline, FusionTarget, which annotates the genomic DNA breakage to RNA and protein sequences, predicts the 3D structures of fusion proteins, and performs comparative virtual screening, comparative molecular dynamics simulation, and quantitative analyses to identify the fusion protein-selective small molecules by selecting drugs with consistent high-fold binding affinity between fusion and wild-type proteins in multiple isoforms. We applied our pipeline to EWSR1::FLI1 in Ewing sarcoma and KMT2A::AFF1 in infant acute lymphoblastic leukemia. Further cell assay experiments confirmed that cells expressing individual fusion genes were more sensitive to the suggested drugs, and the key downstream genes were affected by our drugs. FusionTarget provides a unique foundation for developing therapeutics targeting fusion proteins.

applied computing in medical science↗