QSPRs on photodegradation half-lives of atmospheric chlorinated polycyclic aromatic hydrocarbons associated with particulates
12
Citation
31
Reference
10
Related Paper
Citation Trend
Keywords:
HOMO/LUMO
Photodegradation
Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling of toxicology benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. The second order hyperpolarizability shows the best QSAR model. We also discovered that the nitrobenzene derivative’s substitutional functional group has a significant effect on the quantum molecular descriptors, which reflect the QSAR model.
Hyperpolarizability
Nitrobenzene
Quantum chemical
Molecular descriptor
Quantum Chemistry
Univariate
Cite
Citations (1)
The photodegradation of pesticides was investigated on adsorbed phases: silica, kaolin, bentonite and on a standard soil. Kinetic results show that the photodegradation of phenmedipham is dependant on the nature of the support. On silica and bentonite the degradation is immediate while the photodegradation is slow on kaolin and standard soil. Also, the nature of the photodegradation depends on the reactional mechanism on the support.
Photodegradation
Bentonite
Degradation
Cite
Citations (4)
In the basise on the early study, it has been given that one congruity description on the quantitative structure activity/properties relation(QSAR/QSPR).The important is in trodcing the charateristic on study QSAR/QSPR. Meanwhle, it has been look into the future that the developing foreground on study the QSAR/QSPR on compound molecule's.
Cite
Citations (0)
HOMO/LUMO
Molecular descriptor
Cite
Citations (52)
Polarizability of small Si n (n = 3 to 9) clusters has been calculated using density functional theory (DFT) with Vosko–Wilk–Nusair (VWN) correlation functional. The computed values of polarizability per atom tend to decrease with increasing cluster size. Frequency-dependent polarizability of Si 3 , Si 4 , Si 6 , and Si 9 clusters demonstrates that the polarizability increases slowly with the increase in the energy for energy values less than 1.2 eV but exhibit the same size-dependent features as the static polarizability. The first resonance energy for Si 3 , Si 4 , Si 6 , and Si 9 clusters occur at 1.25, 1.63, 2.5, and 1.98 eV, respectively. Moreover, the anisotropic polarizability is found to be the highest for Si 9 cluster.
Cite
Citations (1)
Molecular polarizability can be used to gain an insight into the origins of polychlorinated dibenzo-p-dioxins (PCDDs) specific binding to their receptor protein. In this work, the effects of the basis set on the polarizability values calculated at the HF level of benzene, chlorobenzene, and dibenzo-p-dioxin (DD) are analyzed. The geometry optimized with the 3-21G basis set and polarizability calculated with the 6-31G(sd,sp) basis set give reliable values useful for comparing polarizabilities of PCDDs with reduced computational costs. The study for two tetrachlorodibenzo-p-dioxin isomers highlights interesting differences in the molecular polarizability tensors as well as in the polarizabilities of C−Cl bonds related to the different chlorine positions on the aromatic skeleton.
Chlorobenzene
Molecular geometry
Basis (linear algebra)
Chlorine atom
Benzene derivatives
Cite
Citations (45)
Polycyclic aromatic hydrocarbons (PAHs) are a group of major contaminants that are ubiquitous in the environment due to their toxicity, mutagenicity and carcinogenicity. This paper studied the soil borne PAHs photodegradation under UV irradiation with phenanthrene(Phe) as target contaminants. The effects of temperature,humic acids(HA)and the intensity of UV irradiation on the Phe photodegradation were investigated. The dynamics of photodegradation of Phe were studied under different conditions. The results show that the rate of Phe photodegradation increases when the temperature rises from 20 ℃ to 30 ℃. The HA played a sensitivizing role during the Phe photodegradation. When the HA concentration was 5 mg·kg-1, HA could efficiently sensitivize the Phe photodegradation. The rate of photodegradation decreases with the decreasing UV irradiation intensity, correlated positively. The half-life increases with the decreasing UV irradiation intensity, correlated negatively.
Photodegradation
Cite
Citations (0)
HOMO/LUMO
Cite
Citations (4)
EU의 REACH 제도 도입에 따라 각종 화학물질에 대한 독성 및 활성 정보 확보를 위해 화학물질의 분자구조 정보를 기반으로 화학물질의 독성 및 활성을 예측하는 정량적구조활성관계(QSAR)에 대한 연구가 최근 활발히 진행되고 있다. QSAR 모델에 사용되는 분자표현자는 매우 다양하기 때문에 화학물질의 물성 및 활성을 잘 표현할 수 있는 주요한 분자표현자를 선택하는 과정은 QSAR 모델 개발에 있어 중요한 부분이다. 본 연구에서는 화학물질의 분자구조 정보를 나타내는 주요 분자표현자의 통계적 선택 방법과 부분최소자승법(Partial least square: PLS) 기반의 새로운 QSAR 모델을 제안하였다. 제안된 QSAR 모델은 130종의 폴리염화바이페닐(Polychlorinated biphenyl: PCB)에 대한 분배계수(log P)와 14종의 PCBs에 대한 반수 치사 농도(Lethal concentration 50%: $LC_{50}$ ) 예측에 사용되고, 제안된 QSAR 모델 예측 정확도는 기존의 OECD QSAR Toolbox에서 제공하는 QSAR 모델과 비교하였다. 관심 화학물질의 분자표현자와 활성정보 간의 높은 상관관계를 갖는 주요 분자표현자를 선별하기 위해서, 상관계수(r)와 variable importance on projections (VIP)기법을 적용하였으며, 화학물질의 독성 및 활성정보를 예측하기 위해 선별된 분자표현자와 활성정보를 이용해 부분최소자승법(PLS)를 사용하였다. 회귀계수($R^2$ )와 prediction residual error sum of square (PRESS)을 이용한 성능평가결과, 제안된 QSAR 모델은 OECD QSAR Toolbox의 QSAR 모델보다 PCBs의 log P와 $LC_{50}$ 에 대하여 각각 26%, 91% 향상된 예측력을 나타내었다. 본 연구에서 제안된 계산독성학 기반의 QSAR 모델은 화학물질의 독성 및 활성정보에 대한 예측력을 향상시킬 수 있고 이러한 방법은 유독 화학물질의 인체 및 환경 위해성 평가에 기여할 것으로 판단된다. Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$ ) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$ ) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.
Molecular descriptor
Applicability domain
Cite
Citations (4)
Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.
Cite
Citations (448)