Internet software for the calculation of the lipophilicity and aqueous solubility of chemical compounds

Multi-order graph attention network for water solubility prediction and interpretation

The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.

The role of herbal plants in the inhibition of SARS-CoV-2 main protease: A computational approach

COVID-19 has quickly spread across the globe, becoming a pandemic. This disease has a variable impact in different countries depending on their cultural norms, mitigation efforts and health infrastructure. This study aims to assess the herbal plants in the pursuit of potential SARS-CoV-2 M^pro inhibitors using in silico approaches. We have considered 16 extracted compounds of 10 different species of these plants. In order to explain their inhibition properties and chemical reactivity pattern, we have performed the density functional theory based calculations of frontier molecular orbitals, molecular electrostatic potential surface and chemical reactivity descriptors. Our calculated lipophilicity, aqueous solubility and binding affinity of the extracted compounds suggest that the inhibition potentials in the order; harsingar > aloe vera > giloy > turmeric > neem > ginger > red onion > tulsi > cannabis > black pepper. On comparing the binding affinity with hydroxychloroquine, we note that the inhibition potentials of the extracts of harsingar, aloe vera and giloy are very promising. In order to validate this, we have also performed MD simulation and MM-PBSA binding free energy analysis. Therefore, we believe that these findings will open further possibilities and accelerate the works towards finding an antidote for this malady.

Cheminformatic analysis of natural product-based drugs and chemical probes

Covering: 1981 to 2019Natural products continue to play a major role in drug discovery, with half of new chemical entities based structurally on a natural product. Herein, we report a cheminformatic analysis of the structural and physicochemical properties of natural product-based drugs in comparison to top-selling brand-name synthetic drugs, and a selection of chemical probes recently discovered from diversity-oriented synthesis libraries. In this analysis, natural product-based drugs covered a broad range of chemical space based on size, polarity, and three-dimensional structure. Natural product-based structures were also more prevalent in top-selling drugs of 2018 compared to 2006. Further, the drugs clustered well according to biosynthetic origins, but less so based on therapeutic classes. Macrocycles occupied distinctive and relatively underpopulated regions of chemical space, while chemical probes largely overlapped with synthetic drugs. This analysis highlights the continued opportunities to leverage natural products and their pharmacophores in modern drug discovery.

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors.

Comparative study of molecular docking, structural, electronic, vibrational and hydrogen bonding interactions on 4-hydroxy benzo hydrazide (4HBH) and its newly designed derivative [(E)–N′-((1H-Pyrrol-2-YL)methylene) –4-hydroxy benzo hydrazide and its isomers (I, II and III)] (potential inhibitors) for COVID-19 protease

Studies have shown that hydrazides and thier derivatives are used for pharmaceutical and medicinal purposes. At present, the whole world is suffering for COVID-19 virus. There are some vaccines or medicines available to treat this disease all over the world. Today the one fourth of the world’s population is under lockdown condition. In this scenario, scientists from the whole world are doing different types of research on this disease. Being a molecular modeller, this inspires us to design new types of species (may be drugs) which may be capable for COVID-19 Protease. In the present effort, we have performed docking studies of title compounds with COVID-19 protein (6LU7) for anti-COVID-19 activity. A comparative quantum chemical calculations of molecular geometries (bond lengths and bond angles) of 4-Hydroxy Benzo Hydrazide (4HBH) and its newly designed derivatve [(E)-N′-((1H-Pyrrol-2-YL)Methylene) –4-Hydroxy Benzo Hydrazide and its isomers (I, II and III)] in the ground state have also been carried out due to its biological importance and compared with the similer type of compound found in literature i.e. benzohydrazide. The optimized geometry and wavenumber of the vibrational bands of the molecules have been calculated by density functional theory (DFT) using Becke’s three-parameters hybrid functional (B3LYP/CAM-B3LYP) with 6–311G (d, p) as the basis set. Vibrational wavenumbers are compared with the observed FT-IR and FTRaman spectra of 4-Hydroxy Benzo Hydrazide. TDDFT calculations are also done on the same level of theory and a theoretical UV-vis spectrum of title molecules are also drawn. HOMO-LUMO analysis has been done to describe the way the molecule interacts with other species. Natural bond orbitals (NBO) analysis has been carried out to inspect the intra- and inter- molecular hydrogen-bonding, conjugative and hyper conjugative interactions and their second order stabilization energy. Nonlinear optical (NLO) analysis has been performed to study the non-linear optical properties of the molecule by computing the first hyperpolarizability. The variation of thermodynamic properties with temperature has been studied. QATIM analysis shows that hydrogen bonding occurs in 4HBH, isomer II and III respectively.

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays

G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c₀ = activity parameter (K_i, IC₅₀, etc.), c₁ = target protein, c₂ = cell line, c₃ = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5-HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [³⁵S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.

Design and Synthesis of Benzimidazole-Chalcone Derivatives as Potential Anticancer Agents

Numerous reports have shown that conjugated benzimidazole derivatives possess various kinds of biological activities, including anticancer properties. In this report, we designed and synthesized 24 new molecules comprising a benzimidazole ring, arene, and alkyl chain-bearing cyclic moieties. The results showed that the N-substituted benzimidazole derivatives bearing an alkyl chain and a nitrogen-containing 5- or 6-membered ring enhanced the cytotoxic effects on human breast adenocarcinoma (MCF-7) and human ovarian carcinoma (OVCAR-3) cell lines. Among the 24 synthesized compounds, (2E)-1-(1-(3-morpholinopropyl)-1H-benzimidazol-2 -yl)-3-phenyl-2-propen-1-one) (23a) reduced the proliferation of MCF-7 and OVCAR-3 cell lines demonstrating superior outcomes to those of cisplatin.

Synthesis of Benzannulated Medium-ring Lactams via a Tandem Oxidative Dearomatization-Ring Expansion Reaction

Medium-ring natural products exhibit diverse biological activities but such scaffolds are underrepresented in probe and drug discovery efforts due to the limitations of classical macrocyclization reactions. We report herein a tandem oxidative dearomatization-ring-expanding rearomatization (ODRE) reaction that generates benzannulated medium-ring lactams directly from simple bicyclic substrates. The reaction accommodates diverse aryl substrates (haloarenes, aryl ethers, aryl amides, heterocycles) and strategic incorporation of a bridgehead alcohol generates a versatile ketone moiety in the products amenable to downstream modifications. Cheminformatic analysis indicates that these medium rings access regions of chemical space that overlap with related natural products and are distinct from synthetic drugs, setting the stage for their use in discovery screening against novel biological targets.

In silico estimation of basic activity-relevant parameters for a set of drug absorption promoters

Finding a balance between a desired drug's potency and its physicochemical properties that are important for its molecule pharmacokinetic or pharmacodynamics profile is still a challenging issue in rational drug discovery. Quantitative assessment of the lipophilic characteristics of potential drug molecules is indispensable for efficient development of Absorption, Distribution, Metabolism, Excretion, Toxicity-tailored structure-activity models; therefore reliable procedures for deriving log P from molecular structure are desirable. In the current work a range of various software log P predictors for estimation of the numerical lipophilic values for a set of cholic acid derivatives were employed and subsequently cross-compared with the experimental parameters. Thus, the empirical lipophilicity (R_M) was compared with the corresponding log P characteristics calculated using alternative methods for deducing the lipophilic features. The mean values of the selected molecular descriptors that were averaged over the chosen calculation methods (consensus clog P) were subsequently correlated with the R_M parameter. As an additional experiment, the iterative variable elimination partial least squares (IVE-PLS) methodology for an ensemble of descriptors retrieved from Dragon 6.0 software was applied for a set of drug transporters. To investigate the variations within the ensemble of cholic acid derivatives principal component analysis (PCA) and self-organizing neural network (SOM) procedures were used to visualize the major differences in the performance of drug promoters with respect to their lipophilic profile.

Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development

Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.

Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles

Studies of the self-aggregation of binary systems are of both theoretical and practical importance. They provide an opportunity to investigate the influence of the molecular structure of the hydrophobe on the nonideality of mixing. On the other hand, linear free energy relationship (LFER) models, such as Hansch's equations, may be used to predict the properties of chemical compounds such as drugs or surfactants. However, the task becomes more difficult once we want to predict simultaneaously the effect over multiple output properties of binary systems of perturbations under multiple input experimental boundary conditions (b(j)). As a consequence, we need computational chemistry or chemoinformatics models that may help us to predict different properties of the autoaggregation process of mixed surfactants under multiple conditions. In this work, we have developed the first model that combines perturbation theory (PT) and LFER ideas. The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference between covariance ΔCov((i)μ(k)) functions before and after multiple perturbations in the binary system. In turn, covariances calculated as the product of two Box-Jenkins operators (BJO) operators. BJOs are used to measure the deviation of the structure of different chemical compounds from a set of molecules measured under a given subset of experimental conditions. The best CPT-LFER model found predicted the effects of 25,000 perturbations over 9 different properties of binary systems. We also reported experimental studies of different experimental properties of the binary system formed by sodium glycodeoxycholate and didodecyldimethylammonium bromide (NaGDC-DDAB). Last, we used our CPT-LFER model to carry out a 1000 data point simulation of the properties of the NaGDC-DDAB system under different conditions not studied experimentally.

Cheminformatic comparison of approved drugs from natural product versus synthetic origins

Despite the recent decline of natural product discovery programs in the pharmaceutical industry, approximately half of all new drug approvals still trace their structural origins to a natural product. Herein, we use principal component analysis to compare the structural and physicochemical features of drugs from natural product-based versus completely synthetic origins that were approved between 1981 and 2010. Drugs based on natural product structures display greater chemical diversity and occupy larger regions of chemical space than drugs from completely synthetic origins. Notably, synthetic drugs based on natural product pharmacophores also exhibit lower hydrophobicity and greater stereochemical content than drugs from completely synthetic origins. These results illustrate that structural features found in natural products can be successfully incorporated into synthetic drugs, thereby increasing the chemical diversity available for small-molecule drug discovery.

Experimental and computational studies of fatty acid distribution networks

A new PT-LFER model is useful for predicting a distribution network in terms of specific fatty acid distribution.

A comparative theoretical study on the biological activity, chemical reactivity, and coordination ability of dichloro-substituted (1,3-thiazol-2-yl)acetamides

We present a theoretical study on three dichloro-substituted (1,3-thiazol-2-yl)acetamides using the first principle density functional approach. Natural bonding orbital analysis is used to discuss the coordination ability of molecules and various global reactivity descriptors are calculated to compare their chemical reactivity. Biological activities of all three molecules are also evaluated. We find that the present molecules show potential coordination ability and their chemical reactivity varies with the position of substitution. We also notice that all three molecules show remarkable biological activities and the (3,4)-dichloro-substituted molecule is relatively more active. The study suggests further investigations on these molecules for their pharmacological importance.

Untargeted metabolomics identifies enterobiome metabolites and putative uremic toxins as substrates of organic anion transporter 1 (Oat1)

Untargeted metabolomics on the plasma and urine from wild-type and organic anion transporter-1 (Oat1/Slc22a6) knockout mice identified a number of physiologically important metabolites, including several not previously linked to Oat1-mediated transport. Several, such as indoxyl sulfate, derive from Phase II metabolism of enteric gut precursors and accumulate in chronic kidney disease (CKD). Other compounds included vitamins (pantothenic acid, 4-pyridoxic acid), urate, and metabolites in the tryptophan and nucleoside pathways. Three metabolites, indoxyl sulfate, kynurenine, and xanthurenic acid, were elevated in the plasma and interacted strongly and directly with Oat1 in vitro with IC50 of 18, 12, and 50 μM, respectively. A pharmacophore model based on several identified Oat1 substrates was used to screen the NCI database and candidate compounds interacting with Oat1 were validated in an in vitro assay. Together, the data suggest a complex, previously unidentified remote communication between the gut microbiome, Phase II metabolism in the liver, and elimination via Oats of the kidney, as well as indicating the importance of Oat1 in the handling of endogenous toxins associated with renal failure and uremia. The possibility that some of the compounds identified may be part of a larger remote sensing and signaling pathway is also discussed.

Modelling, solubility and pK(a) of five sparingly soluble drugs

Drug solubility is an important aspect of drug development. The objective of this investigation was to measure solubilities of five drugs (cimetidine, phenylbutazone, fenbufen, nitrofurantoin, triamterene) at constant pH in range of temperature from 270 to 340K in three solvents: water, ethanol and 1-octanol with the dynamic-visual method and the saturation shake-flask method using spectrophotometric analysis. The Barton group contribution method was used for the calculations of molar volumes of solutes. The thermodynamic description of the solubility curves was made using the thermophysical properties obtained with the differential scanning microcalorimetry technique (DSC). The DSC measurements have shown different than existing in the literature enthalpies of melting for phenylbutazone and fenbufen. The experimental solubility data also differ from the literature data, normally measured at one, or two temperatures only. The solubility data have been correlated by means of three commonly known excess Gibbs energy, G(E) equations. The activity coefficients of drugs at saturated solutions were calculated from the experimental data. Reexamination of the pK(a) values using diluted solutions was made with the Bates-Schwarzenbach method for the pK(a) measurements. The association constants and corresponding pK(a) values of drugs were close to the most of the literature data. We hope that our new solubility data, thermophysical data, and pK(a) values will improve all prediction-methods and their precision.

QSAR-based solubility model for drug-like compounds

Solubility plays a very important role in the selection of compounds for drug screening. In this context, a QSAR model was developed for predicting water solubility of drug-like compounds. First, a set of relevant parameters for establishing a drug-like chemical space was defined. The comparison of chemical structures from the FDAMDD and PHYSPROP databases allowed the selection of properties that were more efficient in discriminating drug-like compounds from other chemicals. These filters were later on applied to the PHYSPROP database and 1174 chemicals fulfilling these criteria and with experimental solubility information available at 25°C were retained. Several QSAR solubility models were developed from this set of compounds, and the best one was selected based on the accuracy of correct classifications obtained for randomly chosen training and validation subsets. Further validation of the model was performed with a set of 102 drugs for which experimental solubility data have been recently reported. A good agreement between the predictions and the experimental values confirmed the reliability of the QSAR model.

Copper-free click chemistry in living animals

Chemical reactions that enable selective biomolecule labeling in living organisms offer a means to probe biological processes in vivo. Very few reactions possess the requisite bioorthogonality, and, among these, only the Staudinger ligation between azides and triarylphosphines has been employed for direct covalent modification of biomolecules with probes in the mouse, an important model organism for studies of human disease. Here we explore an alternative bioorthogonal reaction, the 1,3-dipolar cycloaddition of azides and cyclooctynes, also known as "Cu-free click chemistry," for labeling biomolecules in live mice. Mice were administered peracetylated N-azidoacetylmannosamine (Ac(4)ManNAz) to metabolically label cell-surface sialic acids with azides. After subsequent injection with cyclooctyne reagents, glycoconjugate labeling was observed on isolated splenocytes and in a variety of tissues including the intestines, heart, and liver, with no apparent toxicity. The cyclooctynes tested displayed various labeling efficiencies that likely reflect the combined influence of intrinsic reactivity and bioavailability. These studies establish Cu-free click chemistry as a bioorthogonal reaction that can be executed in the physiologically relevant context of a mouse.

Selective photocatalytic transformations on microporous titanosilicate ETS-10 driven by size and polarity of molecules

Photocatalytic activity of microporous titanosilicate ETS-10 has been studied in water. The photoactivated ETS-10 shows catalytic activity driven by size and polarity of substrates. ETS-10 efficiently catalyzes a conversion of substrates with a size larger than the pore diameter of ETS-10. In contrast, the reactivity of small substrates depends strongly on substrate polarity; less polar substrates show higher reactivity on ETS-10. Electron spin resonance analysis reveals that large substrates or less polar substrates scarcely diffuse inside the highly polarized micropores of ETS-10 and, hence, react efficiently with hydroxyl radicals (*OH) formed on titanol (Ti-OH) groups exposed on the external surface of ETS-10. In contrast, small polar substrates diffuse easily inside the micropores of ETS-10 and scarcely react with *OH, resulting in low reactivity. The photocatalytic activity of ETS-10 is successfully applicable to selective transformations of large reactants or less polar reactants to small polar products, enabling highly selective dehalogenation and hydroxylation of aromatics.

Stability of Atenolol, Acebutolol and Propranolol in Acidic Environment Depending on its Diversified Polarity

The main objective of this research was to investigate the relationship between the polarity of atenolol, acebutolol, and propranolol described by logP and kinetic and thermodynamic parameters characterizing their degradation process in acidic solution. Hydrolysis was carried out in hydrochloric acid at molal concentrations of 0.1 mol/L, 0.5 mol/L, and 1 mol/L for 2 hr at 40 degrees C, 60 degrees C, and 90 degrees C. Chromatographic-densitometric method was used for the determination of drugs under investigation. The identification of degradation products was carried out by using 1H NMR. The degradation processes that occurred in drugs under investigation are described with kinetic parameters (k, t0.1, and t0.5) and energy of activation (Ea). It has been found that the stability of drugs increases toward lipophilic propranolol in the assumed experimental model. The rate constants k decrease, contrary to t0.1, t0.5, and Ea, which vary comparably to logP, thus increasing from the most hydrophilic atenolol, through acebutolol, of lower polarity, to the most lipophilic propranolol. This study demonstrated that the stability of chosen beta-adrenergic blocking agents increases with their lipophilicity.

Virtual computational chemistry laboratory--design and description

Internet technology offers an excellent opportunity for the development of tools by the cooperative effort of various groups and institutions. We have developed a multi-platform software system, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis. The implemented software is based on a three-tier architecture that is one of the standard technologies to provide client-server services on the Internet. The developed software includes several popular programs, including the indices generation program, DRAGON, a 3D structure generator, CORINA, a program to predict lipophilicity and aqueous solubility of chemicals, ALOGPS and others. All these programs are running at the host institutes located in five countries over Europe. In this article we review the main features and statistics of the developed system that can be used as a prototype for academic and industry models.

A hydrophilic azacyclooctyne for Cu-free click chemistry

Biomolecules labeled with azides can be detected through Cu-free click chemistry with cyclooctyne probes, but their intrinsic hydrophobicity can compromise bioavailability. Here, we report the synthesis and evaluation of a novel azacyclooctyne, 6,7-dimethoxyazacyclooct-4-yne (DIMAC). Generated in nine steps from a glucose analogue, DIMAC reacted with azide-labeled proteins and cells similarly to cyclooctynes. However, its superior polarity and water solubility reduced nonspecific binding, thereby improving the sensitivity of azide detection.

Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists

This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-d-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives. A data set of 38 drug-like compounds was coded with 1244 calculated molecular structure descriptors (clustered in 20 data sets) which were obtained from several sources, mainly from Dragon software. Prior to the progress to the ANFIS system, descriptors from the best subsets were selected using unsupervised forward selection (UFS) to eliminate redundancy and multicollinearity followed by fuzzy linear regression algorithm (FLR) which was used for variable selection. ANFIS was applied to train the final descriptors (Mor22m, E3s, R3v+, and R1e+) using a hybrid algorithm consisting of back-propagation and least-square estimation while the optimum number and shape of related functions were obtained through the subtractive clustering algorithm. Comparison of the proposed method with traditional methods, that is, multiple linear regression (MLR) and partial least-square (PLS) was also studied and the results indicated that the ANFIS model obtained from data sets achieved satisfactory accuracy.

In Silico Modeling of Non-Linear Drug Absorption for the P-gp Substrate Talinolol and of Consequences for the Resulting Pharmacodynamic Effect

PURPOSE

The aim of the present work was to demonstrate P-glycoprotein's involvement in the non-linear talinolol pharmacokinetics using an advanced compartment and transit model (ACAT) and to compare the results predicted from the model to the finding of a phase I dose escalation study with oral talinolol doses increasing from 25 to 400 mg.

MATERIALS AND METHODS

Besides minimum input parameters for the compound (pKa(s), solubility at one or more pH's, Peff, doses, formulation, diffusivity), physiological and pharmacokinetic properties, transporter data are included in these predictions. The simulations assumed higher expression levels in lower gastrointestinal regions, in particular in the colon, which is in accordance with the results of intestinal rat perfusion studies and intestinal distribution data from rats, catfishes, micropigs and humans reported in the literature. Optimized values for P-glycoprotein (P-gp) Km and Vmax were used for the final simulation results and for a stochastic virtual trial with 12 patients.

RESULTS

Talinolol, a P-gp substrate, exhibits non-linear dose AUC relationship after administration of 25, 50, 100 and 400 mg immediate-release tablets. This dose dependency is due to a decrease of efflux transport caused by saturation of P-gp by talinolol. It was found that oral bioavailability increases after administration of higher doses of talinolol. The predicted bioavailability of the p.o. 25, 50, 100 and 400 mg doses of talinolol was 64, 76, 85, 94%, respectively. Pharmacokinetic parameters (AUC, Cmax) from in silico simulations are within acceptable range comparing with data, observed in vivo. However, the in vitro value of Km for talinolol's interactions with P-gp could not be used in the simulation and still reproduce the observed non-linear dose dependence. For each of the four doses, GastroPlus was used to model pharmacodynamic (PD) response and to optimize the values of CLe, Emax, and EC5o with the effect compartment linked indirectly to the central compartment. For all simulations, EC50 was 114 nM and E0 was 83 bpm.

CONCLUSION

Comparison between the results of the in vivo study and the in silico simulations determined the quality and reliability of the in silico predictions and demonstrate the simulation of dose dependent absorption. In contrast to previous simulation work for the non-linear dose dependence of interaction with intestinal transporters or enterocyte metabolism, optimized Km and Vmax values were required to reproduce the clinically observed non-linear dose dependence. The model developed may be useful in the prediction of absorption of other P-gp substrates including pharmacodynamic consequences.

A database-centric virtual chemistry system

We describe an Oracle database application for general use within virtual chemistry. The application functions as a central hub and repository for chemical data with interfaces to external calculators. It deals with the general problems of merging data from disparate sources and with scheduling of computational tasks for parallel or sequential execution in a mixed environment. The central database is used for the storage of input, intermediary, and final data as well as for job control. A calculation job is split into distinct tasks, or units of work, which are put in a queue. Tasks are dequeued and handled by specialized calculators. These calculators are in-house or commercial programs for which adaptor modules for connection to the database must be written. Tasks are handled in a transactional fashion, so that uncompleted or failed tasks are left in the queue. This makes the system stable to many types of disturbances. Sorting, filtering, and merging operations are handled by the database itself. Usage is very general, but some specific examples are (1) as a back end for a chemical property calculator Web page, (2) in an automated quantitative structure-activity relationship system, and (3) in virtual screens.

The lipid/protein interface as xenobiotic target site

High-resolution X-ray diffraction structures of integral membrane proteins have revealed various binding modes of lipids, but current spectroscopic studies still use uniform macroscopic binding constants to describe lipid binding. The Adair approach employing microscopic lipid-binding constants has previously been taken to explain the enhancement of agonist binding to the nicotinic acetylcholine receptor by general anaesthetics in terms of the competitive displacement of essential lipid activator molecules [Walcher S, Altschuh J & Sandermann H (2001) J. Biol. Chem.276, 42191-42195]. This approach was extended to tadpole narcosis induced by alcohols. A single class, or two different classes of lipid activator binding sites, are considered. Microscopic lipid and inhibitor binding constants are derived and allow a close fit to dose-response curves of tadpole narcosis on the basis of a preferential displacement of more loosely bound essential lipid activator molecules. This study illustrates the potential of the Adair approach to resolve protein-bound lipid populations.

A quantitative structure-antifungal activity relationship study of oxygenated aromatic essential oil compounds using data structuring and PLS regression analysis

Twenty two oxygenated aromatic essential oil compounds were chosen for the study of the antifungal activity against two wood-decaying fungi, the white-rot Trametes versicolor, which mainly metabolizes lignin, and the brown-rot Coniophoha puteana, which digests cellulose in plant cell walls. Minimal inhibitory concentrations (MICs) were determined by the agar dilution method, using dimethyl sulfoxide (DMSO) as the solvent for the selected compounds and potato-dextrose agar (PDA) as the growth medium for both fungi. The MICs were then used to generate a tree structure, which represents the structuring of the essential oil compounds by the nature and position of the substituents in their aromatic rings, and as dependent variables (log(1/MIC)) in the QSAR analysis. Data structuring proved that a relationship between the molecular structures of the essential oil compounds and their antifungal activity exists, and the hypotheses derived therefrom were complemented by performing a QSAR analysis using the partial least squares (PLS) method. Statistically significant PLS models were obtained with the 1-octanol-water partition coefficient (C log P), the energy of the highest occupied molecular orbital (E(HOMO)), and the number of hydrogen-bond donor atoms in the molecules of the compounds studied (Donor) for T. versicolor and with C log P and the fractional negative surface area (FNSA1) for C. puteana.

Prediction of physicochemical properties based on neural network modelling

The literature describing neural network modelling to predict physicochemical properties of organic compounds from the molecular structure is reviewed from the perspective of pharmaceutical research. The standard three-layer, feed-forward neural network is the technique most frequently used, although the use of other techniques is increasing. Various approaches to describe the molecular structure have been successfully used, including molecular fragments, topological indices, and descriptors calculated by semi-empirical quantum chemical methods. Some physicochemical properties, such as octanol-water partition coefficient, water solubility, boiling point and vapour pressure, have been modelled by several research groups over the years using different approaches and structurally diverse large training sets. The prediction accuracy of most models seems to be rather close to the performance of the experimental measurements, when the accuracy is assessed with a test set from the working database. Results with independent test sets have been less satisfactory. Implications of this problem are discussed.

Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program

This article provides a systematic study of several important parameters of the Associative Neural Network (ASNN), such as the number of networks in the ensemble, distance measures, neighbor functions, selection of smoothing parameters, and strategies for the user-training feature of the algorithm. The performance of the different methods is assessed with several training/test sets used to predict lipophilicity of chemical compounds. The Spearman rank-order correlation coefficient and Parzen-window regression methods provide the best performance of the algorithm. If additional user data is available, an improved prediction of lipophilicity of chemicals up to 2-5 times can be calculated when the appropriate smoothing parameters for the neural network are selected. The detected best combinations of parameters and strategies are implemented in the ALOGPS 2.1 program that is publicly available at http://www.vcclab.org/lab/alogps.

Can the Internet help to meet the challenges in ADME and e-ADME?

The high-throughput screening (HTS) of large proprietary compound collections and combinatorial libraries has put an increased pressure on getting pharmacokinetic and drug metabolism data as early as possible. Properties related to absorption, distribution, metabolism and excretion (ADME) can be estimated by a range of in vivo and in vitro methods. Most are now available or under development in high(er) throughput modus. In addition progress has been made in in silico methods using various QSAR and modeling techniques using a range of recently introduced descriptors tailored to e-ADME. These approaches are promising as a filter for virtual libraries to decide on synthesis as well as in the selection of compounds for acquisition and screening. This paper will discuss a number of Internet resources relevant to ADME studies and predictions. We have focused on areas related to metabolism including metabolic pathways and P450 metabolism, transporters, bioavailability and absorption, pharmacokinetics and pharmacodynamics, molecular properties and tools for data analysis.

Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices

A new method, ALOGPS v 2.0 (http://www.lnh.unil.ch/~itetko/logp/), for the assessment of n-octanol/water partition coefficient, log P, was developed on the basis of neural network ensemble analysis of 12 908 organic compounds available from PHYSPROP database of Syracuse Research Corporation. The atom and bond-type E-state indices as well as the number of hydrogen and non-hydrogen atoms were used to represent the molecular structures. A preliminary selection of indices was performed by multiple linear regression analysis, and 75 input parameters were chosen. Some of the parameters combined several atom-type or bond-type indices with similar physicochemical properties. The neural network ensemble training was performed by efficient partition algorithm developed by the authors. The ensemble contained 50 neural networks, and each neural network had 10 neurons in one hidden layer. The prediction ability of the developed approach was estimated using both leave-one-out (LOO) technique and training/test protocol. In case of interseries predictions, i.e., when molecules in the test and in the training subsets were selected by chance from the same set of compounds, both approaches provided similar results. ALOGPS performance was significantly better than the results obtained by other tested methods. For a subset of 12 777 molecules the LOO results, namely correlation coefficient r(2)= 0.95, root mean squared error, RMSE = 0.39, and an absolute mean error, MAE = 0.29, were calculated. For two cross-series predictions, i.e., when molecules in the training and in the test sets belong to different series of compounds, all analyzed methods performed less efficiently. The decrease in the performance could be explained by a different diversity of molecules in the training and in the test sets. However, even for such difficult cases the ALOGPS method provided better prediction ability than the other tested methods. We have shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data. A comparative performance of the methods as well as a dependence on the number of non-hydrogen atoms in a molecule is also presented.