• Environmental Analysis Health and Toxicology
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• v.30 no.sup
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• pp.7.1-7.10
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• 2015

Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.

• Proceedings of the Korean Society of Life Science Conference
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• 2005.04a
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• pp.151-151
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• 2005

• The Korean Journal of Physiology and Pharmacology
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• v.17 no.3
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• pp.237-243
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• 2013

B13 is a ceramide analogue and apoptosis inducer with potent cytotoxic activity. A series of arylpropyl sulfonamide analogues of B13 were evaluated for their cytotoxicity using MTT assays in prostate cancer PC-3 and leukemia HL-60 cell lines. Some compounds (4, 9, 13, 14, 15, and 20) showed stronger activities than B13 in both tumor cell lines, and compound (15) gave the most potent activity with $IC_{50}$ values of 29.2 and 20.7 ${\mu}M$, for PC-3and HL-60 cells, respectively. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was performed to build highly reliable and predictive CoMSIA models with cross-validated $q^2$ values of 0.816 and 0.702, respectively. Our results suggest that long alkyl chains and a 1R, 2R configuration of the propyl group are important for the cytotoxic activities of arylpropyl sulfonamides. Moreover, the introduction of small hydrophobic groups in the phenyl ring and sulfonamide group could increase biological activity.

• BMB Reports
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• v.31 no.5
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• pp.459-467
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• 1998

A new set of functional group interaction energy descriptors relevant to the ACE (Angiotensin-Converting Enzyme) inhibitory peptide, QSAR (Quantitative Structure Activity Relationships), is presented. The functional group interaction energies approximate the charged interactions and distances between functional groups in molecules. The effective energies of the computationally derived geometries are useful parameters for deriving 3D-QSAR models, especially in the absence of experimentally known active site conformation. ACE is a regulatory zinc protease in the renin-angiotensin system. Therapeutic inhibition of this enzyme has proven to be a very effective treatment for the management of hypertension. The non bond interaction energy values among functional groups of six-feature of ACE inhibitory peptides were used as descriptor terms and analyzed for multivariate correlation with ACE inhibition activity. The functional group interaction energy descriptors used in the regression analysis were obtained by a series of inhibitor structures derived from molecular mechanics and semi-empirical calculations. The descriptors calculated using electrostatic and steric fields from the precisely defined functional group were sufficient to explain the biological activity of inhibitor. Application of the descriptors to the inhibition of ACE indicates that the derived QSAR has good predicting ability and provides insight into the mechanism of enzyme inhibition. The method, functional group interaction energy analysis, is expected to be applicable to predict enzyme inhibitory activity of the rationally designed inhibitors.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.210-218
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• 2004

Poly(ADP-ribose)polymerase-1 (PARP-1) is a nuclear enzyme involved in various physical functions related to genomic repair, and PARP inhibitors have therapeutic application in a variety of neurological diseases. Docking and the QSAR (quantitative structure-activity relationships) studies for 52 PARP-1 inhibitors were conducted using FlexX algorithm, comparative molecular field analysis (CoMFA), and hologram quantitative structure-activity relationship analysis (HQSAR). The resultant FlexX model showed a reasonable correlation (r$^{2}$ = 0.701) between predicted activity and observed activity. Partial least squares analysis produced statistically significant models with q$^{2}$ values of 0.795 (SDEP=0.690, r$^{2}$=0.940, s=0.367) and 0.796 (SDEP=0.678, r$^{2}$ = 0.919, s=0.427) for CoMFA and HQSAR, respectively. The models for the entire inhibitor set were validated by prediction test and scrambling in both QSAR methods. In this work, combination of docking, CoMFA with 3D descriptors and HQSAR based on molecular fragments provided an improved understanding in the interaction between the inhibitors and the PARP. This can be utilized for virtual screening to design novel PARP-1 inhibitors.

• Applied Biological Chemistry
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• v.35 no.1
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• pp.14-22
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• 1992

A series of new N-[1-(benzotriazol-1-yl)aryl]arylamine derivatives were synthesized and their antifungal activities $(pI_{50})$ in vitro against Pyricularia oryzae, Fusarium oxysporum f. sp. sesami, Valsa ceratosperma and Botrytis cinerea were dertermined by the agar medium dilution method. From the results of the quantitative structure-activity relationships $(QSAR_S)$ analysis, $hydrophobicity({\pi})$, $electronic({\Sigma\sigma})$ and molar $refractivity({\Sigma}M_R)$ parameter of X & Y-substituents on the phenyl group were also shown to be important factor in determining the variation in the antifungal activity. 4-Bromo group substituents (1d & 2b) were the most effective compounds and the $half-life(T_{1/2})$ on the hydrolysis of X(1) at netural pH was about 1.5 day. Molecular orbital(MO) functions of substrate compound, linear free energy relationships$(LFER_S)$ on the antifungal reactivity arid the results of molecular design were also discussed.

• Environmental Mutagens and Carcinogens
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• v.21 no.2
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• pp.113-117
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• 2001

The screening of various molecular descriptors for predicting carcinogenic, mutagenic and teratogenic activities of chlorinated aliphatic compounds as drinking water disinfection byproducts (DBPs) has been investigated for the application of quantitative structure-activity relationships (QSAR). The present work embodies the study of relationship between molecular descriptors and toxicity parameters of the genotoxicity endpoints for the screening of relevant molecular descriptors. The toxicity Indices for 29 compounds constituting the testing set were computed by the PASS program and active values were chosen. We investigate feasibility of screening descriptors and of their applications among different genotoxic endpoints. The correlation to teratogenicity of all 29 compounds was significantly improved when the same analysis was done with 20 alkanes only without alkene compounds. The HOMO (highest occupied molecular orbital) energy and number of Cl parameters were dominantly contributed.

• Toxicological Research
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• v.33 no.3
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• pp.173-182
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• 2017

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

• The Korean Journal of Pesticide Science
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• v.7 no.2
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• pp.100-107
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• 2003

To improve the growth inhibitory activity against the shoot and root of rice plant (Oryza sativa L) and barnyard grass (Echinochloa crus-galli), a series of N-substituted phenyl-3,4-dimethylmaleimdes derivatives as substrates were synthesized and then their the inhibitory activities of protoporphyrinogen oxidase (1.3.3.4), protox were measured. The quantitative structure-activity relationships (QSAR) between structures and the inhibitory activities were studied quantitatively using the 2D-QSAR method. And also, molecular sharp similarity between the substrate derivatives and protogen, substrare of protox enzyme were studied. The activities of the two plants indicated that barnyard grass had a higher activity than the rice plant and their correlation relationships have shown in proportion for each. Accordingly, the results of SARs suggest that the electron donating groups as $R_2=Sub.X$ group will bind to phenyl ring because the bigger surface area of negative charged atoms in the substrate molecule derivatives may increase to the higher the activity against barnyard grass. Based on the molecular shape similarity, when the derivatives and protogen, subsbrate of protox enzyme were superimposed by atom fitting, the similarity indices (S) were above 0.8 level but the correlation coefficients (r) between S values and the activities showed not good.

• Journal of Integrative Natural Science
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• v.5 no.1
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• pp.1-5
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• 2012

Quantitative structure-activity relationship (QSAR) methodologies have been applied for many years, to correlate the relationship between physicochemical properties of chemical substances and their biological activities to generate a statistical model for prediction of the activities of new chemical entities. The basic principle behind the QSAR models is that, how structural variation is responsible for the difference in biological activities of the compounds. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which develops the 3D properties of the ligands to predict their biological activities using various chemometric techniques (PLS, G/PLS, ANN etc). It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. This review seeks to provide different 3D-QSAR approaches involved in drug designing process to develop structure-activity relationships and also discussed the fundamental limitations, as well as those that might be overcome with the improved methodologies.