Theoretical descriptors for the correlation of aquatic toxicity of environmental pollutants by quantitative structure-toxicity relationships

Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q2F1: 0.762-0.844; Q2F2: 0.759-0.831 and MAEtest: 0.195-0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides.

Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives

Cancer remains one of the leading causes of death in humans, and new drug substances are therefore being developed. Thus, the anti-cancer activity of xanthene derivatives has become an important topic in the development of new and potent anti-cancer drug substances. Previously published novel series of xanthen-3-one and xanthen-1,8-dione derivatives have been synthesized in one of our laboratories and showed anti-proliferative activity in HeLa cancer cell lines. This series serves as a good basis to develop quantitative structure-activity relationship (QSAR), to study the relations between anti-proliferative activity and chemical structures. A QSAR model has been derived that relies only on two-dimensional molecular descriptors, providing mechanistic insight into the anti-proliferative activity of xanthene derivatives. The model is validated internally and externally and additionally with the set of inactive compounds of the original data, confirming model applicability for the design and discovery of novel xanthene derivatives. The QSAR model is available at the QsarDB repository (http://dx.doi.10.15152/QDB.237).

QSAR modeling with ETA indices for cytotoxicity and enzymatic activity of diverse chemicals

The discharge of huge amount of chemicals from industries into the environment has led to toxicity towards different living species. Therefore, risk assessment of these chemicals is essential. In order to comply with the ethical issues, in this present work, we have developed quantitative structure-activity relationship (QSAR) models for cytotoxicity against GFS (goldfish scale) tissue (Crassius auratus) and enzymatic activity against PLHC-1 cell line (topminnow hepatoma cell line) (Poeciliopsis lucida). The final models were developed by means of PLS (Partial Least Squares) regression method applying only ETA (extended topochemical atom) descriptors. The results obtained from various validation parameters (obtained from the both datasets) suggested that the developed models are statistically robust and predictive. From the insights obtained from the models developed from the Neutral Red dye (NR) dataset, it can be concluded that presence of bulky atoms, unsaturation, branching and hetero atoms (most importantly N, Cl) enhance the cytotoxicity towards the Goldfish scale tissue. On the other hand, in case of the Ethoxyresorufin-O-deethylase (EROD) dataset, presence of higher electronegative atoms (O, Cl), polycyclic aromatic hydrocarbons (PAHs) with more number of rings and absence of polar groups and hydrogen bond acceptors enhance enzymatic activity of the PLHC-1 cell line.

Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets

Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many of them binding affinity constants have been calculated, it is beneficial to analyze the chemical landscape of these families of inhibitors and correlate these inhibition constants with molecular structure descriptors. For this, the HIV-1 RT data was retrieved from the ChEMBL database, carefully curated, and original literature verified, grouped into NRTIs and NNRTIs, analyzed using a hierarchical scaffold classification method and modelled with best multi-linear regression approach. Analysis of the HIV-1 NNRTIs subset results in ten different common structural parent types of oxazepanone, piperazinone, pyrazine, oxazinanone, diazinanone, pyridine, pyrrole, diazepanone, thiazole, and triazine. The same analysis for HIV-1 NRTIs groups structures into four different parent types of uracil, pyrimide, pyrimidione, and imidazole. Each scaffold tree corresponding to the parent types has been carefully analyzed and examined, and changes in chemical structure favorable to potency and stability are highlighted. For both subsets, descriptive and predictive QSAR models are derived, discussed and externally validated, revealing general trends in relationships between molecular structure and binding affinity constants in structurally diverse datasets. Data and QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.202).

Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm

Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from animal tests have been used to assess toxicity of chemicals. However, such methods are ethically questionable since they involve killing and causing suffering of the test animals. Therefore, new in silico methods are being sought to replace the traditional in vivo and in vitro testing methods. In this article we report on one method that can be used to build robust models for the prediction of compounds' properties from their chemical structure. The method has been developed by combining a genetic algorithm, a counter-propagation artificial neural network and cross-validation. It has been tested using existing data on toxicity to fathead minnow (Pimephales promelas). The results show that the method may give reliable results for chemicals belonging to the applicability domain of the developed models. Therefore, it can aid the risk assessment of chemicals and consequently reduce demand for animal tests.

Measurement of baseline toxicity and QSAR analysis of 50 non-polar and 58 polar narcotic chemicals for the alga Pseudokirchneriella subcapitata

In this paper a set of homogenous experimental algal toxicity data was measured for 50 non-polar narcotic chemicals using the alga Pseudokirchneriella subcapitata in a closed test with a growth rate endpoint. Most of the tested compounds are high volume industrial chemicals that so far lacked published REACH-compliant algal growth inhibition values. The test protocol fulfilled the criteria set forth in the OECD guideline 201 and had the same sensitivity as the open test which allowed direct comparison of toxicity values. Baseline QSAR model for non-polar narcotic compounds was established and compared with previous analogous models. Multi-linear QSAR model was derived for the non-polar and 58 previously tested polar (anilines and phenols) narcotic compounds modulating hydrophobicity, molecular size, electronic and molecular stability effects coded in the molecular descriptors. Descriptors in the model were analyzed and applicability domain was assessed providing further guidelines for the in silico prediction purposes in decision support while performing risk assessment. QSAR models in the manuscript are available on-line through QsarDB repository for exploring and prediction services (http://hdl.handle.net/10967/106).

Quantitative relationship between rate constants and molecular structure descriptors for the gas phase hydrogen abstraction reactions

The abstraction of hydrogen by general radicals has a wide role in environmental and also in technological processes because it results in reactive free radicals that play a vital role in atmospheric chemistry and also in biochemical processes. In addition to experimental studies, the theoretical modelling of this elementary reaction has been important for understanding and predicting respective rate constants. In this paper, molecular descriptors in the context of a QSAR approach are used to codify the relationship between molecular structure and rate constants. Unique experimental data is collected from the literature for the reaction R(i)• + R(j)H → R(i)H + R(j)•, where R(i)• = H• and R(j)• are diverse radicals. The four-parameter QSAR model (n = 34, r(2) = 0.81, r(2)(CV) = 0.74, r(2)(scr) = 0.12, s(2) = 0.19) is presented for the bimolecular rate constants, accompanied with model diagnostics and analysis of descriptors in the model.

Chemometrics in Medicine and Pharmacy

This minireview summarizes the basic ways of application of chemometrics in medicine and pharmacy. It brings a collection of applications of chemometric used for the solution of diverse practical problems, e.g. exploitation of biologically active species, effective use of biomarkers, advancement of clinical diagnosis, monitoring of the patient's state and prediction of its perspectives, drug design or classification of toxic chemical substances. The aim of this contribution is a brief presentation of versatile potentialities of contemporary chemometrical techniques and relevant software. They are exemplified by typical cases from literature as well as by own research results of the Chemometrics group at Department of Chemistry, the University of Ss. Cyril & Methodius in Trnava.

Quantitative structure-activity relationship analysis of acute toxicity of diverse chemicals to Daphnia magna with whole molecule descriptors

Quantitative structure-activity relationship analysis and estimation of toxicological effects at lower-mid trophic levels provide first aid means to understand the toxicity of chemicals. Daphnia magna serves as a good starting point for such toxicity studies and is also recognized for regulatory use in estimating the risk of chemicals. The ECOTOX database was queried and analysed for available data and a homogenous subset of 253 compounds for the endpoint LC50 48 h was established. A four-parameter quantitative structure-activity relationship was derived (coefficient of determination, r (2) = 0.740) for half of the compounds and internally validated (leave-one-out cross-validated coefficient of determination, [Formula: see text] = 0.714; leave-many-out coefficient of determination, [Formula: see text] = 0.738). External validation was carried out with the remaining half of the compounds (coefficient of determination for external validation, [Formula: see text] = 0.634). Two of the descriptors in the model (log P, average bonding information content) capture the structural characteristics describing penetration through bio-membranes. Another two descriptors (energy of highest occupied molecular orbital, weighted partial negative surface area) capture the electronic structural characteristics describing the interaction between the chemical and its hypothetic target in the cell. The applicability domain was subsequently analysed and discussed.

Rapid toxicity prediction of organic chemicals to Chlorella vulgaris using quantitative structure-activity relationships methods

This paper presents the results of an optimization study on the toxicity of 91 aliphatic and aromatic compounds as well as a small subset of pesticides to algae Chlorella vulgaris, which was accomplished by using quantitative structure-activity relationships (QSAR). The linear (HM) and the nonlinear method radial basis function neural networks (RBFNN) were used to develop the QSAR models and both of them can give satisfactory prediction results. At the same time, by interpreting the descriptors, we can get some insight into structural features (molecular surface area, electrostatic repulsion, and hydrogen bonds) related to the toxic action. Finally, a detailed analysis on the model application domain defined the compounds, whose estimation can be accepted with confidence. The results of this study suggest that the proposed approaches could be successfully used as a general tool for the estimate of novel toxic compounds.

Quantitative structure-activity relationship (QSAR) modeling of EC50 of aquatic toxicities for Daphnia magna

The experimental EC(50) toxicities toward Daphnia magna for a series of 130 benzoic acids, benzaldehydes, phenylsulfonyl acetates, cycloalkane-carboxylates, benzanilides, and other esters were studied using the Best multilinear regression algorithm (BMLR) implemented in CODESSA. A modified quantitative structure-activity relationships (QSAR) procedure was applied guaranteeing the stability and reproducibility of the results. Separating the initial data set into training and test subsets generated three independent models with an average R(2) of .735. A five-descriptor general model including all 130 compounds, constructed using the descriptors found effective for the independent subsets, was characterized by the following statistical parameters: R(2) = .712; R(2)(cv) = .676; F = 61.331; s(2) = 0.6. The removal of two extreme outliers improved significantly the statistical parameters: R(2) = .759; R(2)(cv) = .728; F = 77.032; s(2) = 0.499. The sensitivity of the general model to chance correlations was estimated by applying a scrambling procedure involving 20 randomizations of the original property values. The resulting R(2) = .192 demonstrated the high robustness of the model proposed. The descriptors appearing in the obtained models are related to the biochemical nature of the adverse effects. An additional study of the EC(50)/LC(50) relationship for a series of 28 compounds (part of our general data set) revealed that these endpoints correlated with R(2) = .98.

QSPR analysis of air-to-blood distribution of volatile organic compounds

Quantitative structure property relationship (QSPR) models for the prediction of human blood:air partition coefficient (log K(blood)) of volatile organic compounds (VOCs) has been developed based on the linear heuristic method (HM) and non-linear radial basis function neural networks (RBFNNs). Molecular descriptors that are calculated from the structures alone were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. RBFNN was performed to obtain more accurate models. Both the linear and the non-linear models can give very satisfactory prediction results: the correlation coefficient R was 0.964 and 0.979, and the root-mean-square (RMS) error was 0.3303 and 0.2542 for the whole data set, respectively. The prediction result of the non-linear model is better than that obtained by the linear model. In addition, this paper provides an effective method for predicting log K(blood) from its structures and gives some insight into the structural features related to the solubility of VOCs in human blood.

Toxicophores and Quantitative Structure -Toxicity Relationships for Some Environmental Pollutants

The electron-conformational (EC) method is employed to reveal the toxicophore and to predict aquatic toxicity quantitatively using as a training set a series of 51 compounds that have aquatic toxicity to fish. By performing conformational analysis (optimization of geometries of the low-energy conformers by the PM3 method) and electronic structure calculations (by ab initio method corrected within the SM54/PM3 solvatation model), the Electron-Conformational Matrix of Congruity (ECMC) was constructed for each conformation of these compounds. The toxicophore defined as the EC sub-matrix of activity (ECSA), a sub-matrix with matrix elements common to all the active compounds under consideration within minimal tolerances, is determined by an iterative procedure of comparison of their ECMC’s, gradually minimizing the tolerances. Starting with only the four most toxic compounds, their ECSA (toxicophore) was found to consists of a 4x4 matrix (four sites with certain electronic and topologic characteristics) which was shown to be present in 17 most active compounds. A structure-toxicity correlation between three toxicophore parameters and the activities of these 17 compounds with R2=0.94 was found. It is shown that the same toxicophore with larger tolerances satisfies the compounds with les activity, thus explicitly demonstrating how the activity is controlled by the tolerances quantitatively and which atoms (sites) are most flexible in this respect. This allows for getting slightly different toxicophores for different levels of activity. For some active compounds that have no toxicophore a bimolecular mechanism of activity is suggested. Distinguished from other QSAR methods, no arbitrary descriptors and no statistics are involved in this EC structure-activity investigation.

The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity

One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R(2)(cv) approximately 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

QSPR prediction of n-octanol/water partition coefficient for polychlorinated biphenyls

The logarithmic n-octanol/water partition coefficient (logK(ow)) is a very important property which concerns water-solubility, bioconcentration factor, toxicity and soil absorption coefficient of organic compounds. Quantitative structure-property relationship (QSPR) model for logK(ow) of 133 polychlorinated biphenyls (PCBs) is analyzed using heuristic method (HM) implemented in CODESSA. In order to indicate the influence of different molecular descriptors on logK(ow) values and well understand the important structural factors affecting the experimental values, three multivariable linear models derived from three groups of different molecular descriptors were built. Moreover, each molecular descriptor in these models was discussed to well understand the relationship between molecular structures and their logK(ow) values. The proposed models gave the following results: the square of correlation coefficient, R(2), for the models with one, two and three molecular descriptors was 0.8854, 0.9239 and 0.9285, respectively.

Study on adsorption behavior of volatile and semivolatile organic vapors to air-dry soils based on QSPR methods

The accurate non-linear quantitive structure-property relationship model for predicting the adsorption constant of volatile and semivolatile organic vapors in soil was firstly developed based on support vector machine (SVM) by using the compounds' molecular descriptors calculated from the structure alone and the features of soil and air. Multiple linear regression (MLR) was used to build the linear QSPR model. Both the linear and non-linear models can give satisfactory prediction results: the correlation coefficient R was 0.953 and 0.995, the mean square error (MSE) was 0.0517 and 0.0057, respectively, for the whole dataset. The prediction result of the SVM model was better than that obtained by the MLR model, which proved non-linear model can simulate the relationship between the structural descriptors, the environmental condition and the soil/air distribution more accurately as well as SVM was a useful tool in the prediction of the adsorption constant of compounds.

Correlation of the toxicity of organic compounds to tadpoles using the Abraham model

The narcotic and lethal concentrations of organic compounds have been compiled for several tadpole species (Rana temporaria, Rana pipiens, Rana japonica, Xenopus laevis and Rana brevipoda porosa). The narcotic and lethal concentrations have been correlated using the Abraham solvation parameter model to yield an equation that can be used to predict the narcotic concentrations of additional nonpolar and polar narcotic compounds to R. temporaria, and a more general correlation that should be applicable to different species of tadpoles. The more general equation is based on 240 experimental data points. A training set of 123 compounds could be fitted with the Abraham solvation parameters with R(2)=0.931 and S.D.=0.343 log units. The training equation predicted the test set of 122 values with AE=-0.022 log units, S.D.=0.300 log units and an average absolute error, AAE, of 0.227 log units. The structural features that are important in narcosis of tadpoles have been examined; it is concluded that hydrogen bond basicity reduces narcotic activity of compounds and that compound size increases narcotic activity. The solvation parameter model enables narcosis of tadpoles to be compared to various other biological processes and to physicochemical processes that might be used as models for narcosis.

Correlating toxicities of organic compounds to select protozoa using the Abraham model

The Abraham solvation parameter model is used to construct mathematical correlations for describing the nonspecific toxicity of organic compounds to three protozoas (Entosiphon sulcantum, Uronema parduczi and Chilomonas paramecium). The derived mathematical correlations describe the observed published toxicity data to within an overall average standard deviation of approximately 0.35 log units. The correlations can be used to estimate aquatic toxicities of organic chemicals to the three aquatic organisms studied, and to help in identifying compounds whose toxic mode of action might involve chemical specific reactivity, rather than nonpolar or polar narcosis. A principal component analysis of the correlation equations found in this work shows that no water-solvent system we have investigated is a good model for nonspecific aquatic toxicity towards the three protozoas. Furthermore, correlation equations for nonspecific aqueous toxicity towards various biological systems, that we have found in this work and in previous studies, cover such a wide range that no single water-solvent system could ever be a good model for all the biological systems.

Quantitative structure-activity relationship for prediction of the toxicity of polybrominated diphenyl ether (PBDE) congeners

Levels of Polybrominated diphenyl ether (PBDEs) are increasing in the environment due to their use as flame retardants. The similarities of structure to polychlorinated biphenyl (PCB) congeners suggest that they may share similar toxicological properties, such as hepatic enzyme induction. In this work, quantitative structure-activity relationship (QSAR) models were constructed based on 406 descriptors for the logarithm of toxicology index (aryl hydrocarbon receptor relative binding affinities, AhR, I) of 18 PBDE congeners. The method used for building model is the Heuristic method, which is included in comprehensive descriptors for structural and statistical analysis (CODESSA) software. The best regression model involved four descriptors, which were related to the conformational changes, atomic reactivity, molecular electrostatic field, and non-uniformity of mass distribution in a molecule of PBDEs, etc. The high square of the correlation coefficient R(2)(0.903) showed the model was satisfactory.

Use of computer-assisted prediction of toxic effects of chemical substances

The current revision of the European policy for the evaluation of chemicals (REACH) has lead to a controversy with regard to the need of additional animal safety testing. To avoid increases in animal testing but also to save time and resources, alternative in silico or in vitro tests for the assessment of toxic effects of chemicals are advocated. The draft of the original document issued in 29th October 2003 by the European Commission foresees the use of alternative methods but does not give further specification on which methods should be used. Computer-assisted prediction models, so-called predictive tools, besides in vitro models, will likely play an essential role in the proposed repertoire of "alternative methods". The current discussion has urged the Advisory Committee of the German Toxicology Society to present its position on the use of predictive tools in toxicology. Acceptable prediction models already exist for those toxicological endpoints which are based on well-understood mechanism, such as mutagenicity and skin sensitization, whereas mechanistically more complex endpoints such as acute, chronic or organ toxicities currently cannot be satisfactorily predicted. A potential strategy to assess such complex toxicities will lie in their dissection into models for the different steps or pathways leading to the final endpoint. Integration of these models should result in a higher predictivity. Despite these limitations, computer-assisted prediction tools already today play a complementary role for the assessment of chemicals for which no data is available or for which toxicological testing is impractical due to the lack of availability of sufficient compounds for testing. Furthermore, predictive tools offer support in the screening and the subsequent prioritization of compound for further toxicological testing, as expected within the scope of the European REACH program. This program will also lead to the collection of high-quality data which will broaden the database for further (Q)SAR approaches and will in turn increase the predictivity of predictive tools.

QSARs in ecotoxicological risk assessment

The need for more ecotoxicological data encourages the use of QSARs because of the reduction of (animal) testing, time and cost. QSARs may however only be used if they prove to be reliable and accurate. In this paper, four QSARs were attempted to predict toxicity for 170 compounds from a broad chemical class, using them as a black-box. Predictions were obtained for 122 compounds, indicating an important drawback of QSARs, i.e., for 28% of the compounds QSARs cannot be used at all. Ecosar, Topkat, and QSARs for non-polar and polar narcosis generated predictions for 120, 39, 24, and 11 compounds, respectively. Correlations between experimental and predicted effect concentrations were significant for Topkat and the QSAR for polar narcosis, but generally poor for Ecosar and the QSAR for non-polar narcosis. When predicted effect concentrations for fish were allowed to deviate from experimental values by a factor of 5, correct predictions were generated for 77%, 54%, 68%, and 91% of the compounds using Ecosar, Topkat, and the QSARs for non-polar and polar narcosis, respectively. It was impossible to indicate specific chemical classes for which a QSAR should be used or not. The results show that currently available QSARs cannot be used as a black-box.

The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine

The heuristic method (HM) and support vector machine (SVM) were used to build the linear and nonlinear quantitive structure-property relationship (QSPR) models for the prediction of the fish bioconcentration factors (BCF) for 122 diverse nonionic organic chemicals using the three descriptors calculated from the molecular structure alone and selected by HM. Both the linear and nonlinear model can give very satisfactory prediction results: the square of correlation coefficient R(2) was 0.929 and 0.953, the root mean square (RMS) error was 0.404 and 0.331, respectively for the whole dataset. The prediction result of the SVM model is better than that obtained by heuristic method, which proved SVM was a useful tool in the prediction of the BCF. At the same time, the HM model showed the influencing degree of different molecular descriptors on bioconcentration factors and then could improve the understanding for the bioconcentration mechanism of organic pollutants from molecular level.

QSPR analysis for infinite dilution activity coefficients of organic compounds

A quantitative structure-property relationship study of the infinite-dilution activity coefficients for a set of 38 organic compounds in ionic liquids such as 1-methyl-3-ethylimidazolium bis((trifluoromethyl)sulfonyl)imide, 1,2-dimethyl-3-ethylimidazolium bis((trifluoromethyl)-sulfonyl)imide, and 4-methyl-N-butylpyridinium tetrafluoroborate. QSPR study was carried out using the CODESSA PRO program. A general three-parameter QSPR model was obtained. Three orthogonal theoretical molecular descriptors satisfactorily correlate with the activity coefficients. The descriptors, such as the complementary information content, the fractional partial negative surface area and the count of hydrogen donor sites describe the dilution process in ILs.

Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines

The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

Accurate Quantitative Structure−Property Relationship Model To Predict the Solubility of C60 in Various Solvents Based on a Novel Approach Using a Least-Squares Support Vector Machine

A least-squares support vector machine (LSSVM) was used for the first time as a novel machine-learning technique for the prediction of the solubility of C60 in a large number of diverse solvents using calculated molecular descriptors from the molecular structure alone and on the basis of the software CODESSA as inputs. The heuristic method of CODESSA was used to select the correlated descriptors and build the linear model. Both the linear and the nonlinear models can give very satisfactory prediction results: the square of the correlation coefficient R(2) was 0.892 and 0.903, and the root-mean-square error was 0.126 and 0.116, respectively, for the whole data set. The prediction result of the LSSVM model is better than that obtained by the heuristic method and the reference, which proved LSSVM was a useful tool in the prediction of the solubility of C60. In addition, this paper provided a new and effective method for predicting the solubility of C60 from its structures and gave some insight into the structural features related to the solubility of C60 in different solvents.

Chemical Toxicity Correlations for Several Fish Species Based on the Abraham Solvation Parameter Model

The Abraham solvation parameter model is used to construct mathematical correlations for describing the nonspecific aquatic toxicity of organic compounds to the fathead minnow, guppy, bluegill, goldfish, golden orfe, and high-eyes medaka. The derived mathematical correlations describe the observed published toxicity data to within an overall average standard deviation of approximately 0.28 log units. In the case of ester solutes, the descriptions were improved by introducing an indicator variable into the basic model. Derived correlations can be used to estimate aquatic toxicities of organic chemicals to the six fish species studied and to help in identifying compounds whose toxic mode of action might involve chemical specific reactivity, rather than nonpolar or polar narcosis. A principal component analysis of the correlation equations shows that the water-octanol system is a poor model for nonspecific aquatic toxicity but that the water-isobutanol and water-pentanol systems are much better models.

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION

To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process.

RESULTS

We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.

The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine

Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R(2) was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.

QSPR treatment of rat blood:air, saline:air and olive oil:air partition coefficients using theoretical molecular descriptors

A QSPR treatment has been applied to a data set that consists of 100 diverse organic compounds to relate the logarithmic function of rat blood:air, saline:air and olive oil:air partition coefficients (denoted by log K(b:a), log K(s:a), and log K(o:a), respectively), with theoretical molecular and fragment descriptors. Three QSPR models with squared correlation coefficients of 0.881, 0.926, and 0.922, respectively, were obtained. The verification of the predictive power of these models on a test set of 33 organic chemicals that were not included in the training set gave satisfactory squared correlation coefficients: 0.791 for rat blood:air, 0.794 for saline:air and 0.846 for olive oil:air.

QSARs for chemical mutagens from structure: ridge regression fitting and diagnostics

QSAR models have been developed for a diverse set of mutagens using computed molecular descriptors. Such models can be used in predicting mutagenicity from structure. All common methods-regression, neural nets, k-nearest neighbors-are 'linear smoothers'-weighted averages of the activities in the calibration data with weights dependent on the descriptors. While they have been studied extensively, a vital but overlooked area is 'case diagnostics', pointers to compounds that are poorly fitted, or are unusually influential in fitting the model. This is particularly true where the measured activity is binary-present or absent. We illustrate the use of numeric and graphic diagnostics, particularly that of the FF plot, with a data set with 508 compounds and 307 structural descriptors used to predict mutagenicity.