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At least 109 records · Page 6Linked to original sources
PubMed2006

Fatty acid amide hydrolase inhibitors from virtual screening of the endocannabinoid system.

The endocannabinoid system consists of two cannabinoid receptors (CB1 and CB2), endogenous ligands (endocannabinoids), and the enzymes involved in the metabolism of the endocannabinoids, including fatty acid amide hydrolase (FAAH) and monoglyceride lipase (MGL). In the present study, virtual screening of MGL inhibitors was performed by utilizing a comparative model of the human MGL enzyme. All hit molecules were tested for their potential MGL inhibitory activity, but no compounds were found capable of inhibiting MGL-like enzymatic activity in rat cerebellar membranes. However, these compounds were also tested for their potential FAAH inhibitory activity and five compounds (2-6) inhibiting FAAH were found with IC50 values between 4 and 44 microM. In addition, the hit molecules from the virtual screening of CB2 receptor ligands (reported previously in Salo et al. J. Med. Chem. 2005, 48, 7166) were also tested in our FAAH assay, and four active compounds (7-10) were found with IC50 values between 0.52 and 22 microM. Additionally, compound 7 inhibited MGL-like enzymatic activity with an IC50 value of 31 microM.

Amidohydrolases↗
PubMed2005

Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the alpha1A adrenergic receptor.

In this paper, we describe homology modeling of the alpha1A receptor based on the X-ray structure of bovine rhodopsin. The protein model has been generated by applying ligand-supported homology modeling, using mutational and ligand SAR data to guide the protein modeling procedure. We performed a virtual screening of the company's compound collection to test how well this model is suited to identify alpha1A antagonists. We applied a hierarchical virtual screening procedure guided by 2D filters and three-dimensional pharmacophore models. The ca. 23,000 filtered compounds were docked into the alpha1A homology model with GOLD and scored with PMF. From the top-ranked compounds, 80 diverse compounds were tested in a radioligand displacement assay. 37 compounds revealed K(i) values better than 10 microM; the most active compound binds with 1.4 nM to the alpha1A receptor. Our findings suggest that rhodopsin-based homology models may be used as the structural basis for GPCR lead finding and compound optimization.

Adrenergic alpha-1 Receptor Antagonists↗
PubMed2004

Virtual screening with flexible docking and COMBINE-based models. Application to a series of factor Xa inhibitors.

A two-step, fully automatic virtual screening procedure consisting of flexible docking followed by activity prediction by COMparative BINding Energy (COMBINE) analysis is presented. This novel approach has been successfully applied, as an example with medicinal chemistry interest, to a recently reported series of 133 factor Xa (fXa)(1) inhibitors whose activities encompass 4 orders of magnitude. The docking algorithm is linked to the COMBINE analysis program and used to derive independent regression models of the 133 inhibitors docked within three different fXa structures (PDB entries 1fjs, 1f0r, and 1xka), so as to explore the effect of receptor conformation on the overall results. Reliable docking conformations and predictive regression models requiring eight latent variables could be derived for two of the fXa structures, with the best model achieving a Q(2) of 0.63 and a standard deviation of errors of prediction (SDEP) of 0.51 (leave-one-out). The two-step procedure was then employed to screen a designed virtual library of 112 ligands, containing both active and inactive compounds. While docking energies alone could show a good performance for selecting hits, including structurally diverse ones, inclusion of COMBINE analysis regression models provided improved rankings for the identification of structurally related molecules in external sets. In our best case, a recognition rate of approximately 80% of known binders at approximately 15% false positives rate was achieved, corresponding to an enrichment factor of approximately 450% over random.

Algorithms↗
PubMedDate not supplied

NIPALSTREE: a new hierarchical clustering approach for large compound libraries and its application to virtual screening.

A hierarchical clustering algorithm--NIPALSTREE--was developed that is able to analyze large data sets in high-dimensional space. The result can be displayed as a dendrogram. At each tree level the algorithm projects a data set via principle component analysis onto one dimension. The data set is sorted according to this one dimension and split at the median position. To avoid distortion of clusters at the median position, the algorithm identifies a potentially more suited split point left or right of the median. The procedure is recursively applied on the resulting subsets until the maximal distance between cluster members exceeds a user-defined threshold. The approach was validated in a retrospective screening study for angiotensin converting enzyme (ACE) inhibitors. The resulting clusters were assessed for their purity and enrichment in actives belonging to this ligand class. Enrichment was observed in individual branches of the dendrogram. In further retrospective virtual screening studies employing the MDL Drug Data Report (MDDR), COBRA, and the SPECS catalog, NIPALSTREE was compared with the hierarchical k-means clustering approach. Results show that both algorithms can be used in the context of virtual screening. Intersecting the result lists obtained with both algorithms improved enrichment factors while losing only few chemotypes.

Algorithms↗
PubMedDate not supplied

Ligand bias of scoring functions in structure-based virtual screening.

A total of 945 known actives and roughly 10 000 decoy compounds were docked to eight different targets, and the resulting poses were scored using 10 different scoring functions. Three different score postprocessing methods were evaluated with respect to improvement of the enrichment in virtual screening. The three procedures were (i) multiple active site correction (MASC) as has been proposed by Vigers and Rizzi, (ii) a variation of MASC where corrections terms are predicted from simple molecular descriptors through PLS, PLS MASC, and (iii) size normalization. It was found that MASC did not generally improve the enrichment factors when compared to uncorrected scoring functions. For some combinations of scoring functions and targets, the enrichment was improved, for others not. However, by excluding the standard deviation from the MASC equation and transforming the scores for each target to a mean of 0 and a standard deviation of 1 (unit variance normalization), the performance was improved as compared to the original MASC method for most combinations of targets and scoring functions. Furthermore, when the molecular descriptors were fit to the mean scores over all targets and the resulting PLS models were used to predict mean scores, the enrichment as compared to the raw score was improved more often than by straightforward MASC. A high to intermediate linear correlation between the score and the number of heavy atoms was found for all scoring functions except FlexX. There seems to be a correlation between the size dependence of a scoring function and the effectiveness of PLS MASC in increasing the enrichment for that scoring function. Finally, normalization by molecular weight or heavy atom count was sometimes successful in increasing the enrichment. Dividing by the square or cubic root of the molecular weight or heavy atom count instead was often more successful. These results taken together suggest that ligand bias in scoring functions is a source of false positives in structure-based virtual screening. The number of false positives caused by ligand bias may be decreased using, for example, the PLS MASC procedure proposed in this study.

Binding Sites↗
PubMed2006

Virtual screening for aryl hydrocarbon receptor binding prediction.

The overall goal of this study has been to validate computational models for predicting aryl hydrocarbon receptor (AhR) binding. Due to the unavailability of the AhR X-ray crystal structure we have decided to use QSARs models for the binding prediction virtual screening. We have built up CoMFA, Volsurf, and HQSAR models using as a training set 84 AhR ligands. Additionally, we have built a hybrid model combining two of the final selected models in order to give a single operational system. The results show that CoMFA, VolSurf, HQSAR, and the hybrid models gives good results (R(2) equal to 0.91, 0.79, 0.85, and 0.82 and q(2) 0.62, 0.58, 0.62, and 0.70, respectively). Since the techniques analyzed show a good correlation and good prediction also for an external test set, particularly the HQSAR and the hybrid model, we can conclude that these models can be used for predicting AhR binding in virtual screening.

Dioxins↗
PubMed2005

Discovering novel chemical inhibitors of human cyclophilin A: virtual screening, synthesis, and bioassay.

Cyclophilin A (CypA) is a member of cyclophilins, a family of the highly homologous peptidyl prolyl cis-trans isomerases (PPIases), which can bind to cyclosporin A (CsA). CypA plays critical roles in various biological processes, including protein folding, assembly, transportation, regulation of neuron growth, and HIV replication. The discovery of CypA inhibitor is now of a great special interest in the treatment of immunological disorders. In this study, a series of novel small molecular CypA inhibitors have been discovered by using structure-based virtual screening in conjunction with chemical synthesis and bioassay. The SPECS_1 database containing 85,000 small molecular compounds was searched by virtual screening against the crystal structure of human CypA. After SPR-based binding affinity assay, 15 compounds were found to show binding affinities to CypA at submicro-molar or micro-molar level (compounds 1-15). Seven compounds were selected as the starting point for the further structure modification in considering binding activity, synthesis difficulty, and structure similarity. We thus synthesized 40 new small molecular compounds (1-6, 15, 16a-q, 17a-d, and 18a-l), and four of which (compounds 16b, 16h, 16k, and 18g) showed high CypA PPIase inhibition activities with IC50s of 2.5-6.2 microM. Pharmacological assay indicated that these four compounds demonstrated somewhat inhibition activities against the proliferation of spleen cells.

Animals↗
PubMedDate not supplied

Measuring CAMD technique performance: a virtual screening case study in the design of validation experiments.

The dynamic nature and comparatively young age of computational chemistry is such that novel algorithms continue to be developed at a rapid pace. Such efforts are often wrought at the expense of extensive experimental validations of said techniques, preventing a deeper understanding of their potential utility and limitations. Here we address this issue for ligand-based virtual screening descriptors through design of validation experiments that better reflect the aims of real world application. Applying the newly defined chemotype enrichment approach, a variety of two- and three-dimensional (2D/3D) similarity descriptors have been compared extensively across data sets from four diverse target types. The inhibitors within said data sets contain molecules exhibiting a wide array of substructure functionality, size and flexibility, permitting descriptor comparison in myriad settings. Relative descriptor performance under these conditions is examined, including results obtained using more typical virtual screening validation experiments. Guidelines for optimal application of said descriptors are also discussed in the context of the results obtained, as is the potential utility of fingerprint filtering.

Computer-Aided Design↗
PubMed2003

Improving structure-based virtual screening by multivariate analysis of scoring data.

Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.

Acetylcholinesterase↗
PubMed2005

BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. 1. Alignment and virtual screening applications.

We have developed a fast grid-based algorithm, BRUTUS, for rigid-body molecular superposition and similarity searching. BRUTUS aligns molecules using field information derived from charge distributions and van der Waals shapes of the compounds. Molecules can have similar biological properties if their charge distributions and shapes are similar, even though they have different chemical structures; that is, BRUTUS can identify compounds possessing similar properties, regardless of their structures. In this paper, we present two applications of BRUTUS. First, BRUTUS was used to superimpose five sets of inhibitors. Second, two sets of known inhibitors were searched from a database, and the results were analyzed using self-organizing maps. We demonstrate that BRUTUS is successful in superimposing compounds using molecular fields and, importantly, is fast and accurate enough for virtual screening of chemical databases using a standard personal computer. This fast and efficient molecular-field-based algorithm is applicable for virtual screening of structurally diverse, active molecules.

Algorithms↗
PubMed2005

Tools for building a comprehensive modeling system for virtual screening under real biological conditions: The Computational Titration algorithm.

Computational tools utilizing a unique empirical modeling system based on the hydrophobic effect and the measurement of logP(o/w) (the partition coefficient for solvent transfer between 1-octanol and water) are described. The associated force field, Hydropathic INTeractions (HINT), contains much rich information about non-covalent interactions in the biological environment because of its basis in an experiment that measures interactions in solution. HINT is shown to be the core of an evolving virtual screening system that is capable of taking into account a number of factors often ignored such as entropy, effects of solvent molecules at the active site, and the ionization states of acidic and basic residues and ligand functional groups. The outline of a comprehensive modeling system for virtual screening that incorporates these features is described. In addition, a detailed description of the Computational Titration algorithm is provided. As an example, three complexes of dihydrofolate reductase (DHFR) are analyzed with our system and these results are compared with the experimental free energies of binding.

1-Octanol↗
PubMed2006

Virtual screening for environmental pollutants: structure-activity relationships applied to a database of industrial chemicals.

The current risk paradigm calls for individual consideration and evaluation of each separate environmental pollutant, but this does not reflect accurately the cumulative impact of anthropogenic chemicals. In the present study, previously validated structure-activity relationships were used to estimate simultaneously the baseline toxicity and atmospheric persistence of approximately 50,000 compounds. The results from this virtual screening indicate fairly stable statistical distributions among small anthropogenic compounds. The baseline toxicity was not changed much by halogen substitution, but a distinct increase seemed to occur in the environmental persistence with increased halogenation. The ratio of the atmospheric half-lives to the median lethal concentrations provides a continuous scale with which to rank and summarize the incremental environmental impacts in a mixture-exposure situation. Halogenated compounds as a group obtained a high ranking in this data set, with well-known pollutants at the very top: DDT metabolites and derivatives, polychlorinated biphenyls, diphenyl ethers and dibenzofurans, chlorinated paraffins, chlorinated benzenes and derivatives, hydrochlorofluorocarbons, and dichlorononylphenol. Environmentally friendly chemicals that obtained the lowest rank are nearly all hydroxylated and water-soluble. Virtual screening can assist with "green chemistry" in designing safe and degradable products and enable assessment of the efficiency in chemicals risk management.

Chemical Industry↗
PubMedDate not supplied

Recursive median partitioning for virtual screening of large databases.

Recently, we have introduced the median partitioning (MP) method for diversity selection and compound classification. The MP approach utilizes property descriptors with continuous value ranges, transforms these descriptors into a binary classification scheme by determining their medians in source databases, and divides database molecules in subsequent steps into populations above or below these medians. Having previously demonstrated the usefulness of MP for the classification of molecules according to biological activity, we have now gone a step further and extended the methodology for application in virtual screening. In these calculations, a series of bait molecules having desired activity is added to large compound databases, and subsequent iterations or recursions are carried out to reduce the number of candidate molecules until a small number of compounds are found in partitions enriched with bait molecules. For each recursion step, descriptor combinations are identified that copartition as many active molecules as possible. Descriptor selection is facilitated by application of a genetic algorithm (GA). The recursive MP approach (RMP) has been applied to five diverse biological activity classes in virtual screening of a database consisting of approximately 1.34 million molecules to which different types of active compounds were added. RMP analysis produced hit rates of up to 21%, dependent on the biological activity class, and led to an average approximately 3600-fold improvement over random selection for the activity classes that were used as test cases.

Algorithms↗
PubMed2004

Integration of virtual screening into the drug discovery process.

Advances in high-throughput virtual screening using docking, predictive ADME methods and their integration with informatics and high-performance computing are reviewed. Docking approaches have led to the identification of novel active compounds. Predictive ADME methods have improved on selective test sets with broader training sets, though extensive validation is lacking.

Combinatorial Chemistry Techniques↗
PubMed2003

A 3D model of SARS_CoV 3CL proteinase and its inhibitors design by virtual screening.

AIM: To constructed a three-dimensional (3D) model for the 3C like (3CL) proteinase of SARS coronavirus (SARS-CoV), and to design inhibitors of the 3CL proteinase based on the 3D model. METHODS: Bioinformatics analyses were performed to search the homologous proteins of the SARS-CoV 3CL proteinase from the GenBank and PDB database. A 3D model of the proteinase was constructed by using homology modeling technique. Targeting to the 3D model and its X-ray crystal structure of the main proteinase (Mpro) of transmissible gastroenteritis virus (TGEV), virtual screening was performed employing molecular docking method to identify possible 3CL proteinase inhibitors from small molecular databases. RESULTS: Sequence alignment indicated that the SARS-CoV 3CL proteinase was extremely homologous to TGEV Mpro, especially the substrate-binding pocket (active site). Accordingly, a 3D model for the SARS-CoV 3CL proteinase was constructed based on the crystal structure of TGEV Mpro. The 3D model adopts a similar fold of the TGEV Mpro, its structure and binding pocket feature are almost as same as that of TGEV Mpro. The tested virtual screening indicated that 73 available proteinase inhibitors in the MDDR database might dock into both the binding pockets of the TGEV Mpro and the SARS-CoV 3CL proteinase. CONCLUSIONS: Either the 3D model of the SARS-CoV 3CL proteinase or the X-ray crystal structure of the TGEV Mpro may be used as a starting point for design anti-SARS drugs. Screening the known proteinase inhibitors may be an appreciated shortcut to discover anti-SARS drugs.

Computational Biology↗
PubMed2025

Genomic mapping of diabetic kidney disease biomarkers and identification of potential inhibitors through virtual screening.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus, marked by a multifactorial pathogenesis and the absence of sensitive diagnostic biomarkers. Identifying novel molecular targets and therapeutic options is essential to improve early diagnosis and treatment outcomes. METHODS: To uncover potential biomarkers and therapeutic candidates, we performed an integrated genomic analysis using microarray and RNA-seq datasets from the Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) databases. Differentially expressed genes (DEGs) were identified and subjected to protein-protein interaction (PPI) network analysis. Key genes were further explored through virtual screening of an FDA-approved compound library using molecular docking techniques. Drug-likeness was assessed via Lipinski's rule of five. RESULTS: A total of 40 DEGs were identified, among which ISCU (downregulated; involved in iron-sulfur cluster biogenesis) and AP1S2 (upregulated; associated with vesicular trafficking) emerged as potential biomarkers. PPI analysis revealed their involvement in critical DKD-related pathways, such as extracellular matrix remodeling and oxidative stress. Virtual screening identified six FDA-approved compounds with high binding affinity (≤-7.96 kcal/mol) to ISCU, notably ZINC000001576020, all of which complied with Lipinski's rule. CONCLUSIONS: This in-silico study nominates ISCU and AP1S2 as candidate diagnostic biomarkers for DKD and identifies computationally prioritized inhibitors targeting ISCU. These findings require experimental validation but provide a molecular framework for precision diagnosis and therapeutic development. These findings offer new molecular insights that could inform precision diagnosis and personalized treatment strategies for diabetic kidney disease.

Diabetic Nephropathies↗
PubMedDate not supplied

ProPose: steered virtual screening by simultaneous protein-ligand docking and ligand-ligand alignment.

The 'model-free' screening engine ProPose implements a general method for performing simultaneous protein-ligand docking, ligand-ligand alignment, pharmacophore queries-and combinations thereof-in order to incorporate a priori information into screening protocols. In this manuscript we describe a case study on herpes simplex virus thymidine kinase, an important antiviral drug target, where we evaluate different approaches for handling a specific type of a priori information, i.e., multiple target structures. We demonstrate that a simultaneous alignment on two target structures--in conjunction with logic operations on interactions and docking constraints derived from protein structure--is an effective means of (i) improving the enrichment of chemical substructures that are compatible with the a priori known ligands, (ii) ensuring the steric fit into the target protein, and (iii) handling target flexibility. The combination of ligand- and receptor-based methods steers the virtual screening by ranking molecules according to the similarity of their interaction pattern with known ligands, thereby--to some extent--outweighing the deficiencies of simple scoring functions often used in initial virtual screening.

Binding Sites↗