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PubMed2006

Discovery of potent inhibitors of pseudomonal quorum sensing via pharmacophore modeling and in silico screening.

HipHop-Refine was employed to derive a binding hypothesis for pseudomonal quorum sensing (QS) antagonists. The model was employed as 3D search query to screen the National Cancer Institute (NCI) database. One of the hits illustrated nanomolar QS inhibitory activity. The fact that this compound contained tetravalent lead (Pb) prompted us to evaluate the activities of phenyl mercuric nitrate and thimerosal, both fit the binding pharmacophore. The two mercurials illustrated nanomolar to low micromolar IC50 inhibitory values against pseudomonal QS. The three compounds represent a new class of QS inhibitors.

Algorithms↗
PubMed2026

In silico screening of anti-atherosclerotic compounds from Morus alba leaves by machine learning and network pharmacology.

OBJECTIVE: This study integrates machine learning with network pharmacology, molecular docking, and molecular dynamics simulations to screen bioactive compounds from Mulberry leaves and elucidate their potential mechanisms against atherosclerosis (AS). METHODS: A training dataset of anti-AS active compounds was compiled and encoded as Morgan fingerprints. Three machine learning classifiers, specifically Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XG-Boost), were constructed and evaluated using multiple performance metrics. Potential active components from Mulberry leaves and AS-related targets were retrieved, followed by protein-protein interaction network construction and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was then performed to evaluate binding affinities between core targets and candidate compounds, and the most stable complex was subjected to molecular dynamics simulations using GROMACS (2025). RESULTS: The RF model achieved superior performance (accuracy= 0.8354, F1 = 0.8408, AUC = 0.9119) with 100% external validation accuracy. Thirteen anti-AS candidates were prioritized from mulberry leaves, four of which have been previously documented. Network pharmacology revealed AKT1 and IL6 as core targets, enriched in pathways such as endocrine resistance. Molecular docking and dynamics simulations confirmed strong binding between oxysanguinarine and AKT1, with the complex exhibiting high stability. CONCLUSION: The RF model provides a reliable computational tool for prioritizing anti-AS compounds from Mulberry leaves. The integrated analysis reveals that Mulberry leaves exert anti-atherosclerotic effects through multi-target (e.g., AKT1, IL6) and multi-pathway (e.g., PI3K-Akt) mechanisms, offering a framework for further experimental validation.

Morus↗
PubMed2006

Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening.

A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential (r = 0.87,F-statistic = 69.13,r(BS)2 = 0.76,r(LOO)2 = 0.68). The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database.

Algorithms↗
PubMed2005

In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software.

Quantitative structure-activity relationship (QSAR) software offers a rapid, cost effective means of prioritizing the mutagenic potential of chemicals. MDL QSAR models were developed using atom-type E-state indices and non-parametric discriminant analysis. Models were developed for Salmonella typhimurium gene mutation, combining results from strains TA97, TA98, TA100, TA1535, TA1536, TA1537, and TA1538 (n=3228), and Escherichia coli gene mutation tests WP2, WP100, and polA (n=472). Composite microbial mutation models (n=3338) were developed combining all Salmonella, E. coli, and the Bacillus subtilis rec spot test study results. The datasets contained 74% non-pharmaceuticals and 26% pharmaceuticals. Salmonella and microbial mutagenesis external validation studies included a total of 1444 and 1485 compounds, respectively. The average specificity, sensitivity, positive predictivity, concordance, and coverage of Salmonella models was 76, 81, 73, 78, and 98%, respectively, with similar performance for the microbial mutagenesis models. MDL QSAR and discriminant analysis provides rapid and highly automated mutagenicity screening software with good specificity, sensitivity, and coverage that is simpler and requires less user intervention than other similar software. MDL QSAR modules for microbial mutagenicity can provide efficient and cost effective large scale screening of compounds for mutagenic potential for the chemical and pharmaceutical industry.

Algorithms↗
PubMed2001

In silico screening for tumour-specific expressed sequences in human genome.

A computer-based differential display tool named HsAnalyst has been developed and successfully used for the comparison of expression patterns in a set of tumours versus a set of normal tissues. A list of EST clusters highly represented in tumours and rarely observed in normal tissues has been developed as a resulting output file of the program. These differentially expressed EST clusters (genes) can be useful for developing new tumour markers and prognostic indicators for a wide set of human malignancies. Tumour-specific protein-coding genes may be considered a manifestation of tumour-specific gene expression.

Databases, Genetic↗
PubMedDate not supplied

Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa.

Factor Xa inhibitors are innovative anticoagulant agents that provide a better safety/efficacy profile compared to other anticoagulative drugs. A chemical feature-based modeling approach was applied to identify crucial pharmacophore patterns from 3D crystal structures of inhibitors bound to human factor Xa (Pdb entries 1fjs, 1kns, 1eqz) using the software LIGANDSCOUT and CATALYST. The complex structures were selected regarding the criteria of high inhibitory potency (i.e. all ligands show K(i) values against factor Xa in the subnanomolar range) and good resolution (i.e. at least 2.2 A) in order to generate selective and high quality pharmacophore models. The resulting chemical-feature based hypotheses were used for virtual screening of commercial molecular databases such as the WDI database. Furthermore, a ligand-based molecular modeling approach was performed to obtain common-feature hypotheses that represent the relevant chemical interactions between 10 bioactive factor Xa inhibitors and the protein, respectively. In a next step a virtual combinatorial library was designed in order to generate new compounds with similar chemical and spatial properties as known inhibitors. The software tool ILIB DIVERSE was used for this procedure in order to provide new scaffolds of this group of anticoagulants. Finally we present the combination of these two techniques, hence virtual screening was performed with selective pharmacophore models in a focused virtual combinatorial database. De novo derived molecular scaffolds that were able to adequately satisfy the pharmacophore criteria are revealed and are promising templates for candidates for further development.

Anticoagulants↗
PubMedDate not supplied

Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors.

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.

Algorithms↗
PubMedDate not supplied

An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands.

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.

Artificial Intelligence↗
PubMed2002

Identification of potent and novel alpha4beta1 antagonists using in silico screening.

The antigen alpha4beta1 (very late antigen-4, VLA-4) plays an important role in the migration of white blood cells to sites of inflammation. It has been implicated in the pathology of a variety of diseases including asthma, multiple sclerosis, and rheumatoid arthritis. We describe a series of potent inhibitors of alpha4beta1 that were discovered using computational screening for replacements of the peptide region of an existing tetrapeptide-based alpha4beta1 inhibitor (1; 4-[N'-(2-methylphenyl)ureido]phenylacetyl-Leu-Asp-Val) derived from fibronectin. The search query was constructed using a model of 1 that was based upon the X-ray conformation of the related integrin-binding region of vascular cell adhesion molecule-1 (VCAM-1). The 3D search query consisted of the N-terminal cap and the carboxyl side chain of 1 because, upon the basis of existing structure-activity data on this series, these were known to be critical for high-affinity binding to alpha4beta1. The computational screen identified 12 reagents from a virtual library of 8624 molecules as satisfying the model and our synthetic filters. All of the synthesized compounds tested inhibit alpha4beta1 association with VCAM-1, with the most potent compound having an IC(50) of 1 nM, comparable to the starting compound. Using CATALYST, a 3D QSAR was generated that rationalizes the variation in activities of these alpha4beta1 antagonists. The most potent compound was evaluated in a sheep model of asthma, and a 30 mg nebulized dose was able to inhibit early and late airway responses in allergic sheep following antigen challenge and prevented the development of nonspecific airway hyperresponsiveness to carbachol. Our results demonstrate that it is possible to rapidly identify nonpeptidic replacements of integrin peptide antagonists. This approach should be useful in identification of nonpeptidic alpha4beta1 inhibitors with improved pharmacokinetic properties relative to their peptidic counterparts.

Administration, Inhalation↗
PubMed2004

Identification of non-phosphate-containing small molecular weight inhibitors of the tyrosine kinase p56 Lck SH2 domain via in silico screening against the pY + 3 binding site.

The protein p56 lymphoid T cell tyrosine kinase (Lck) is predominantly expressed in T lymphocytes where it plays a critical role in T-cell-mediated immune response. Lck participates in phosphotyrosine-dependent protein-protein interactions through its modular binding unit, the Src homology-2 (SH2) domain. Accordingly, virtual screening methods combined with experimental assays were used to identify small molecular weight nonpeptidic compounds that block Lck SH2 domain-dependent interactions. Virtual screening included scoring normalization procedures and postdocking structural clustering that is shown to facilitate the selection of active compounds. By targeting the well-defined hydrophobic binding pocket known to impart specificity on Lck-protein interactions (i.e., pY + 3 site), inhibitors of the Lck SH2 domain were discovered that omit the phosphotyrosine (pY) or related moieties. The 34 out of 196 computationally selected compounds were shown to inhibit Lck SH2 domain association with phosphorylated immunoreceptor tyrosine based activation motifs peptide. Twenty-four of the active compounds were further tested for their ability to modulate biological function. Thirteen of these compounds showed inhibitory activity in mixed lymphocyte culture assay. Fluorescence titration experiments on four of these active compounds further verified their binding to the SH2 domain. Because of their simple chemical structures, these small organic compounds have the potential to act as lead compounds for the development of novel immunosuppressant drugs.

Animals↗
PubMed2004

Receptor flexibility in the in silico screening of reagents in the S1' pocket of human collagenase.

A major difficulty in structure-based molecular design is the prediction of the structure of the protein-ligand complex because of the enormous number of degrees of freedom. Commonly, the target protein is kept rigid in a single low-energy conformation. However, this does not reflect the dynamic nature of protein structures. In this work, we investigate the influence of receptor flexibility in virtual screening of reagents on a common scaffold in the S1' pocket of human collagenase (matrix metalloproteinase-1). We compare screening using a single-crystal structure and multiple NMR structures, both apo and holo forms. We also investigate two computational methods of addressing receptor flexibility that can be used when NMR data are not available. The results from virtual screening using the experimental structures are compared to those obtained using the two computational methods. From the results, we draw conclusions about the impact of target flexibility on the identification of active and diverse reagents in a virtual screening protocol.

Apoenzymes↗
PubMed2003

In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system.

Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked indescending order according to their predicted mutagenic potential and the first 100 chemicals with highest mutagenicity scores are presented. The screening result offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.

Animals↗
PubMed2006

Grid-enabled high-throughput in silico screening against influenza A neuraminidase.

Encouraged by the success of the first EGEE biomedical data challenge against malaria (WISDOM), the second data challenge battling avian flu was kicked off in April 2006 to identify new drugs for the potential variants of the influenza A virus. Mobilizing thousands of CPUs on the Grid, the six-week-long high-throughput screening activity has fulfilled over 100 CPU years of computing power and produced around 600 gigabytes of results on the Grid for further biological analysis and testing. In the paper, we demonstrate the impact of a worldwide Grid infrastructure to efficiently deploy large-scale virtual screening to speed up the drug design process. Lessons learned through the data challenge activity are also discussed.

Binding Sites↗
PubMed2004

In silico screening of a saturated mutation library of tomato.

A comprehensive mutant population is a basic resource for exploring gene function. We developed an isogenic tomato 'mutation library' in the genetic background of the inbred variety M82. A total of 13 000 M(2) families, derived from EMS (ethyl methane sulfonate) and fast-neutron mutagenesis, were visually phenotyped in the field and categorized into a morphological catalog that includes 15 primary and 48 secondary categories. Currently, 3417 mutations have been cataloged; among them are most of the previously described phenotypes from the monogenic mutant collection of The Tomato Genetics Resource Center, and over a thousand new mutants, with multiple alleles per locus. The phenotypic database indicates that most mutations fall into more than a single category (pleiotropic), with some organs such as leaves more prone to alterations than others. All data and images can be searched and accessed in the Solanaceae Genome Network (SGN) on a site called 'The Genes That Make Tomatoes' (http://zamir.sgn.cornell.edu/mutants/).

Crosses, Genetic↗
PubMedDate not supplied

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening.

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.

Pharmaceutical Preparations↗
PubMed2005

Multiple target screening method for robust and accurate in silico ligand screening.

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].

Databases, Factual↗
PubMed2004

TOMOCOMD-CARDD, a novel approach for computer-aided 'rational' drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds.

In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silico screening and several compounds from the Merck Index, Negwer's handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100%) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.

Animals↗