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PubMed2005

In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method.

Quantitative Structure-Property Relationship models (QSPR) based on in vivo blood-brain permeation data (logBB) of 88 diverse compounds, 324 descriptors and a systematic variable selection method, namely 'Variable Selection and Modeling method based on the prediction (VSMP)', are reported. Of all the models developed using VSMP, the best three-descriptors model is based on Atomic type E-state index (SsssN), AlogP98 and Van der Waal's surface area (r=0.8425, q=0.8239, F=68.49 and SE=0.4165); the best four-descriptors model is based on Kappa shape index of order 1, Atomic type E-state index (SsssN), Atomic level based AI topological descriptor (AIssssC) and AlogP98 (r=0.8638, q=0.8472, F=60.982 and SE=0.3919). The performance of the models on three test sets taken from the literature is illustrated and compared with the results from other reported computational approaches. Test set III constitutes 91 compounds from the literature with known qualitative BBB indication and is used for virtual screening studies. The success rate of the reported models is 82% in the case of BBB+ compounds and a similar success rate is observed with BBB- compounds. Finally, as the models reported herein are based on computed properties, they appear as a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Blood-Brain Barrier↗
PubMed2005

A computer-based approach to the rational discovery of new trichomonacidal drugs by atom-type linear indices.

Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.

Antitrichomonal Agents↗
PubMed2005

Application of the in combo screening approach for the discovery of non-alkaloid acetylcholinesterase inhibitors from Cichorium intybus.

Because of the direct correlation of cholinergic deficit and the severity of dementia, Alzheimer's disease is preferentially treated with acetylcholinesterase (AChE) inhibitors to supplement the acetylcholine level. In this study we focused on non-alkaloid AChE inhibitors from natural sources in order to discover new lead structures. In the course of in vitro extract screening of Tyrolean plants using an enzyme assay with Ellman's reagent, the dichloromethane extract of chicory roots (Cichorium intybus L.) showed a pronounced inhibitory effect on AChE. At a concentration of 1 mg extract/ml an inhibition of 70% was measured. Based on a 3D multi-conformational molecular-structure database consisting of secondary metabolites from C. intybus known from the relevant literature, virtual screening filtering experiments were conducted using both a feature-based pharmacophore model and a docking procedure. Some low molecular weight sesquiterpenoids exhibited distinct interactions with the pharmacophore model. In order to verify the applicability of this computer-aided strategy, an activity-guided fractionation of the chicory root extract was performed, which resulted in the isolation of two sesquiterpene lactones, 8-deoxylactucin and lactucopicrin, showing significant and dose-dependent inhibitory activity on AChE (IC(50) of 308.1 microM [CI(95) 243.9 - 405.3 microM] and 150.3 microM [CI(95) 100.8 - 188.1 microM], respectively). The two isolates were correctly predicted within the virtual screening process which corroborates the potential of the computer-assisted in combo screening approach for the discovery of the anti-cholinesterase compounds from C. intybus.

Acetylcholinesterase↗
PubMed2000

In silico studies for the rational discovery of anticonvulsant compounds.

Theoretical models to virtual screening and rational design of anticonvulsant compounds based on a topological substructural molecular design (TOSS-MODE) approach are developed. These models, developed on the basis of data sets of great structural variability, permit the classification of compounds as active/inactive anticonvulsants and predict the quantitative anticonvulsant potency of such compounds. The classification model is applied to a virtual screening of anticonvulsant compounds by analyzing a data set of molecules reported in the literature. More than 88% of them were well classified by the current model. Active and inactive fragments are identified by using the present approach. Some of the active fragments are identified in anticonvulsant molecules as potential pharmacophores and one of them is analyzed in detail. The three-dimensional (3-D) features of this fragment are investigated in a series of five anticonvulsant compounds. Some structure-anticonvulsant activity relationships are derived on the basis of the 3-D structure of this fragment and some findings reported in the literature that indicate that it is an important pharamacophore are outlined.

Anticonvulsants↗
PubMed2004

"Teaching old drugs to kill new bugs": structure-based discovery of anti-SARS drugs.

Severe acute respiratory syndrome (SARS) main protease or 3C-like protease (3CLpro) is essential for the propagation of the coronaviral life cycle and is regarded as one of the main targets for structure-based anti-SARS drug design. It is an attractive approach to find new uses for old drugs as they have already been through extensive clinical testing and could easily be accelerated for clinical approval. Briefly, we performed virtual screening of a database of small molecules against SARS 3CLpro, analyzed inhibitor-protease complexes, and identified several covalent and non-covalent inhibitors. Several old drugs that bind to SARS 3CLpro active site were selected and in silico derivatized to generate covalent irreversible inhibitors with enhanced affinity. Furthermore, we show that pharmacophores derived from clusters of compounds resulting out of virtual screening could be useful probes for future structure-activity relationship studies (SARs) and fine-tune the lead molecules identified.

Antiviral Agents↗
PubMedDate not supplied

Ligand-based combinatorial design of selective purinergic receptor (A2A) antagonists using self-organizing maps.

A virtual screening procedure based on a topological pharmacophore similarity metric and self-organizing maps (SOM) was developed and applied to optimizing combinatorial products functioning as P(1) purinergic receptor antagonists. The target was the human A(2A) receptor. A SOM was developed using a set of biologically tested molecules to establish a preliminary structure-activity relationship. A combinatorial library design was performed by projecting virtually assembled new molecules onto the SOM. A small focused library of 17 selected combinatorial products was synthesized and tested. On average, the designed structures yielded a 3-fold smaller binding constant ( approximately 33 vs approximately 100 nM) and 3.5-fold higher selectivity (50 vs 14) than the initial library. The most selective compound obtained revealed a 121-fold relative selectivity for A(2A) with K(i) (A(2A)) = 2.4 nM, and K(i) (A(1)) = 292 nM. This result demonstrates that it was possible to design a small, activity-enriched focused library with an improved property profile using the SOM virtual screening approach. The strategy might be particularly useful in projects in which structure-based design cannot be applied because of a lack of receptor structure information, for example, in the many projects aiming at finding new GPCR modulators.

Adenosine A2 Receptor Antagonists↗
PubMed2007

In silico drug discovery: solving the "target-rich and lead-poor" imbalance using the genome-to-drug-lead paradigm.

Advances in genomics, proteomics, and structural genomics have identified a large number of protein targets. Virtual screening has gained popularity in identifying drug leads by computationally screening large numbers of chemicals against experimentally determined protein targets. In that context, there continues to be a "target-rich and lead-poor" imbalance, reflecting an insufficiency of chemists pursuing drug discovery in academia, the challenge of engaging more chemists in this area of research, and a paucity of available protein target structures. This imbalance in manpower and structural information can be ameliorated, in part, by adapting a "genome-to-drug-lead" approach, in which chemicals can be virtually screened against computer-predicted protein targets, within the context of the US National Science Foundation's petascale computing initiative. This approach offers a solution to reduce manpower requirements for more chemists to experimentally search for drug leads, which represent one of the greatest limitations to drug discovery and better exploits the extensive availability of drug targets at the gene level, ultimately improving the success of moving discoveries from the laboratory to the patient.

Chemistry, Pharmaceutical↗
PubMed2005

The Tropical Biominer Project: mining old sources for new drugs.

The Tropical Biominer Project is a recent initiative from the Federal University of Minas Gerais (UFMG) and the Oswaldo Cruz foundation, with the participation of the Biominas Foundation (Belo Horizonte, Minas Gerais, Brazil) and the start-up Homologix. The main objective of the project is to build a new resource for the chemogenomics research, on chemical compounds, with a strong emphasis on natural molecules. Adopted technologies include the search of information from structured, semi-structured, and non-structured documents (the last two from the web) and datamining tools in order to gather information from different sources. The database is the support for developing applications to find new potential treatments for parasitic infections by using virtual screening tools. We present here the midpoint of the project: the conception and implementation of the Tropical Biominer Database. This is a Federated Database designed to store data from different resources. Connected to the database, a web crawler is able to gather information from distinct, patented web sites and store them after automatic classification using datamining tools. Finally, we demonstrate the interest of the approach, by formulating new hypotheses on specific targets of a natural compound, violacein, using inferences from a Virtual Screening procedure.

Animals↗
PubMed2025

Oncogenic EME1 promotes tumor progression and immune modulation in human cancers with therapeutic targeting potential.

BACKGROUND: EME1, a critical DNA repair endonuclease, has emerged as a potential oncogene implicated in genome instability and cancer progression. However, its pan-cancer roles, prognostic significance, immune interactions, and therapeutic targeting remain underexplored. METHODS: We conducted a comprehensive pan-cancer analysis integrating multi-omics data from public databases, including TIMER2.0, GEPIA2, TISIDB, and cBioPortal, to evaluate EME1 expression, genetic alterations, and their association with clinical outcomes, immune infiltration, and molecular pathways. Virtual screening of 3180 FDA-approved drugs and molecular dynamics (MD) simulations were employed to identify and validate potential EME1 inhibitors. RESULTS: EME1 was significantly overexpressed in various human cancers and positively associated with advanced tumor grade and stage. High EME1 expression and mutations were linked to poor overall and disease-free survival. Immunogenomic profiling revealed strong positive correlations between EME1 and myeloid-derived suppressor cells (MDSCs), alongside a negative association with endothelial cell function, suggesting immunosuppressive roles. Machine learning models based on EME1-associated genes demonstrated high predictive accuracy for liver hepatocellular carcinoma (AUC > 0.90). Virtual screening identified eight promising drug candidates, including Everolimus and Dioscin, with strong binding affinities. MD simulations confirmed the stability of these interactions, particularly for Dioscin. CONCLUSION: This study reveals the multifaceted oncogenic roles of EME1 in tumor progression, immune evasion, and prognosis. It proposes EME1 as a promising biomarker and therapeutic target across multiple cancer types. The identified drug candidates warrant further in vitro and in vivo validation for potential repurposing in EME1-targeted cancer therapy.

EME1↗
PubMed2026

Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder associated with substantial disability. Although genome-wide association studies have identified multiple BD-associated loci, the underlying genes and mechanisms remain incompletely understood. METHODS: We integrated a European-ancestry BD genome-wide association dataset with cross-tissue and tissue-specific transcriptome-wide association studies (TWAS) and complementary gene-based analysis. Candidate genes were further evaluated using differential expression analysis, consensus clustering, immune infiltration analysis, machine learning, summary-data-based Mendelian randomization, Mendelian randomization using single-cell expression quantitative trait locus data, single-nucleus transcriptomics, phenome-wide association analysis, and virtual screening. RESULTS: The integrative analyses prioritized 37 candidate genes. Peripheral-blood differential-expression analysis identified 14 genes that remained significant after FDR correction, and their expression profiles separated BD samples into two expression-defined clusters. Machine-learning analysis selected UNC50, LMAN2L, LYG2, HSPE1, and KANSL3 for an exploratory classification nomogram. SMR associated genetically predicted higher HSPE1 expression with increased BD risk in two blood eQTL datasets. Cell-type-specific analyses indicated HSPE1-related associations in T-cell and natural killer cell subsets, while single-nucleus analysis descriptively showed higher HSPE1 expression in medial thalamic T cells from BD samples. PheWAS identified no genome-wide significant associations for HSPE1, whereas virtual screening identified candidate compounds with favorable predicted docking scores against the HSPE1 structure. CONCLUSION: This integrative multi-omics study identified HSPE1 as a candidate BD risk gene with immune-cell-related regulatory evidence, providing insight into BD pathogenesis and supporting functional validation.

Humans↗
PubMed2000

Prediction of quinolone activity against Mycobacterium avium by molecular topology and virtual computational screening.

We conducted a quantitative structure-activity relationship study using a database of 158 quinolones previously tested against Mycobacterium avium-M. intracellulare complex in order to develop a model capable of predicting the activity of new quinolones against the M. avium-M. intracellulare complex in vitro. Topological indices were used as structural descriptors and were related to anti-M. avium-M. intracellulare complex activity by using the linear discriminant analysis (LDA) statistical technique. The discriminant equation thus obtained correctly classified 137 of the 158 quinolones, including 37 of a test group of 44 randomly chosen compounds. This model was then applied to 24 quinolones, including recently developed fluoroquinolones, whose MICs were subsequently determined in vitro by using the Alamar blue microplate assay; the biological results confirmed the model's predictions. The MICs of these 24 quinolones were then treated by multilinear regression (MLR) to establish a model capable of classifying them according to their in vitro activities. Using this model, a good correlation between measured and predicted MICs was found (r(2) = 0.88; r(2)(cv) [cross-validation correlation] = 0.82). Moxifloxacin, sparfloxacin, and gatifloxacin were the most potent against the M. avium- M. intracellulare complex, with MICs of 0.2, 0.4, and 0.9 microg/ml, respectively. Finally, virtual modifications of these three drugs were evaluated in LDA and MLR models in order to determine the importance of different substituents in their activity. We conclude that the combination of molecular-topology methods, LDA, and MLR provides an excellent tool for the design of new quinolone structures with enhanced activity.

4-Quinolones↗
PubMed2006

Structure-based discovery of inhibitors of the YycG histidine kinase: new chemical leads to combat Staphylococcus epidermidis infections.

BACKGROUND: Coagulase-negative Staphylococcus epidermidis has become a major frequent cause of infections in relation to the use of implanted medical devices. The pathogenicity of S. epidermidis has been attributed to its capacity to form biofilms on surfaces of medical devices, which greatly increases its resistance to many conventional antibiotics and often results in chronic infection. It has an urgent need to design novel antibiotics against staphylococci infections, especially those can kill cells embedded in biofilm. RESULTS: In this report, a series of novel inhibitors of the histidine kinase (HK) YycG protein of S. epidermidis were discovered first using structure-based virtual screening (SBVS) from a small molecular lead-compound library, followed by experimental validation. Of the 76 candidates derived by SBVS targeting of the homolog model of the YycG HATPase_c domain of S. epidermidis, seven compounds displayed significant activity in inhibiting S. epidermidis growth. Furthermore, five of them displayed bactericidal effects on both planktonic and biofilm cells of S. epidermidis. Except for one, the compounds were found to bind to the YycG protein and to inhibit its auto-phosphorylation in vitro, indicating that they are potential inhibitors of the YycG/YycF two-component system (TCS), which is essential in S. epidermidis. Importantly, all these compounds did not affect the stability of mammalian cells nor hemolytic activities at the concentrations used in our study. CONCLUSION: These novel inhibitors of YycG histidine kinase thus are of potential value as leads for developing new antibiotics against infecting staphylococci. The structure-based virtual screening (SBVS) technology can be widely used in screening potential inhibitors of other bacterial TCSs, since it is more rapid and efficacious than traditional screening technology.

Amino Acid Sequence↗
PubMed2004

A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance.

A thorough evaluation of some of the most advanced docking and scoring methods currently available is described, and guidelines for the choice of an appropriate protocol for docking and virtual screening are defined. The generation of a large and highly curated test set of pharmaceutically relevant protein-ligand complexes with known binding affinities is described, and three highly regarded docking programs (Glide, GOLD, and ICM) are evaluated on the same set with respect to their ability to reproduce crystallographic binding orientations. Glide correctly identified the crystallographic pose within 2.0 A in 61% of the cases, versus 48% for GOLD and 45% for ICM. In general Glide appears to perform most consistently with respect to diversity of binding sites and ligand flexibility, while the performance of ICM and GOLD is more binding site-dependent and it is significantly poorer when binding is predominantly driven by hydrophobic interactions. The results also show that energy minimization and reranking of the top N poses can be an effective means to overcome some of the limitations of a given docking function. The same docking programs are evaluated in conjunction with three different scoring functions for their ability to discriminate actives from inactives in virtual screening. The evaluation, performed on three different systems (HIV-1 protease, IMPDH, and p38 MAP kinase), confirms that the relative performance of different docking and scoring methods is to some extent binding site-dependent. GlideScore appears to be an effective scoring function for database screening, with consistent performance across several types of binding sites, while ChemScore appears to be most useful in sterically demanding sites since it is more forgiving of repulsive interactions. Energy minimization of docked poses can significantly improve the enrichments in systems with sterically demanding binding sites. Overall Glide appears to be a safe general choice for docking, while the choice of the best scoring tool remains to a larger extent system-dependent and should be evaluated on a case-by-case basis.

Algorithms↗
PubMed2004

[Molecular design and biological activity of BCR-ABL tyrosine kinase inhibitors].

OBJECTIVE: To discover BCR-ABL tyrosine kinase inhibitors through structure based virtual screening. METHODS: Docking screening against the distinctive inactive conformation of the catalytic domain of BCR-ABL tyrosine kinase was performed on 3D database. The MTT assay was performed to assess the viability of the tumor cells treated with selected compounds. The amount and kinase activity of BCR-ABL protein were detected in the presence of compounds by Western blot analysis and immunoprecipitation. RESULTS: From the top 1,000 compounds with the best DOCK energy score, 15 compounds were selected for biological assay. Eight out of 15 compounds showed notable inhibitory activity against Ph+ human K562 cells with IC50 values ranging from 10 to 200 micromol/L. In cell-based assays of ABL tyrosine phosphorylation, the ability of two kinds of novel, structurally diverse, lead compounds to inhibit ABL kinase activity was observed. However, no significant differences in the amount of BCR-ABL protein were noted on the ABL immunoblot in the presence of these lead compounds. CONCLUSIONS: Two promising lead compounds were discovered to inhibit BCR-ABL tyrosine kinase activity. Virtual screening technique has been proven to narrow down the size of screening compound libraries to the most prospective drug candidates with high success rates.

Computer Simulation↗
PubMed2002

Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase.

Many proteins undergo small side chain or even backbone movements on binding of different ligands into the same protein structure. This is known as induced fit and is potentially problematic for virtual screening of databases against protein targets. In this report we investigate the limits of the rigid protein approximation used by the docking program, GOLD, through cross-docking using protein structures of influenza neuraminidase. Neuraminidase is known to exhibit small but significant induced fit effects on ligand binding. Some neuraminidase crystal structures caused concern due to the bound ligand conformation and GOLD performed poorly on these complexes. A 'clean' set, which contained unique, unambiguous complexes, was defined. For this set, the lowest energy structure was correctly docked (i.e. RMSD < 1.5 A away from the crystal reference structure) in 84% of proteins, and the most promiscuous protein (1mwe) was able to dock all 15 ligands accurately including those that normally required an induced fit movement. This is considerably better than the 70% success rate seen with GOLD against general validation sets. Inclusion of specific water molecules involved in water-mediated hydrogen bonds did not significantly improve the docking performance for ligands that formed water-mediated contacts but it did prevent docking of ligands that displaced these waters. Our data supports the use of a single protein structure for virtual screening with GOLD in some applications involving induced fit effects, although care must be taken to identify the protein structure that performs best against a wide variety of ligands. The performance of GOLD was significantly better than the GOLD implementation of ChemScore and the reasons for this are discussed. Overall, GOLD has shown itself to be an extremely good, robust docking program for this system.

Algorithms↗
PubMed2006

Molecular descriptors and methods for ligand based virtual high throughput screening in drug discovery.

The aim of virtual high throughput screening is the identification of biologically relevant molecules amongst either tangible or virtual (large) collections of compounds. Amongst the various virtual screening approaches, those that are ligand based are becoming very popular due to the possibility to screen millions of molecules in a timely way. Descriptors and methods are briefly introduced and reviewed with more emphasis for those approaches that are based on fingerprint descriptors and that seems to be more utilized during the drug discovery process.

Artificial Intelligence↗
PubMed2002

Exhaustive identification of human class II basic helix-loop-helix proteins by virtual library screening.

Cellular proliferation, specification and differentiation in developing tissues are tightly coordinated by groups of transcription factors in response to extrinsic and intrinsic signals. Furthermore, renewable pools of stem cells in adult tissues are subject to similar regulation. Basic helix-loop-helix (bHLH) proteins are a group of transcription factors that exert such a determinative influence on a variety of developmental pathways from C. elegans to humans, and we wished to exclusively identify novel members from within the whole human bHLH family. We have, therefore, developed an 'empirical custom fingerprint', to define the class II bHLH domain and exclusively identify these proteins in silico. We have identified nine previously uncharacterised human class II proteins, four of which were novel, by interrogating conceptual translations of the GenBank HTGS database. RT-PCR and mammalian 2-hybrid analysis of a subset of the factors demonstrated that they were indeed expressed, and were able to interact with an appropriate binding partner in vitro. Thus, we are now approaching an almost complete listing of human class II bHLH factors.

Amino Acid Sequence↗
PubMed2002

Exhaustive identification of human class II basic helix-loop-helix proteins by virtual library screening.

Cellular proliferation, specification and differentiation in developing tissues are tightly coordinated by groups of transcription factors in response to extrinsic and intrinsic signals. Furthermore, renewable pools of stem cells in adult tissues are subject to similar regulation. Basic helix-loop-helix (bHLH) proteins are a group of transcription factors that exert such a determinative influence on a variety of developmental pathways from C. elegans to humans, and we wished to exclusively identify novel members from within the whole human bHLH family. We have, therefore, developed an 'empirical custom fingerprint', to define the class II bHLH domain and exclusively identify these proteins in silico. We have identified nine previously uncharacterised human class II proteins, four of which were novel, by interrogating conceptual translations of the GenBank HTGS database. RT-PCR and mammalian 2-hybrid analysis of a subset of the factors demonstrated that they were indeed expressed, and were able to interact with an appropriate binding partner in vitro. Thus, we are now approaching an almost complete listing of human class II bHLH factors.

Amino Acid Sequence↗