Developing predictive models for toxicity of organic chemicals to green algae based on mode of action

Bakire, Serge; Yang, Xinya; Ma, Guangcai; Wei, Xiaoxuan; H, Yu; Chen, Jianrong; Lin, Hongjun

doi:10.1016/j.chemosphere.2017.10.028

Cited by 47 publications

(11 citation statements)

References 43 publications

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“…Molecular descriptors that characterize electronic and structural properties of compounds have provided useful predictive information on the toxicity of compounds (Bakire et al, 2018).…”

Section: Synthesis and Characterizationmentioning

confidence: 99%

Synthesis and characterization of polyethylene terephthalate (PET) precursors and potential degradation products: Toxicity study and application in discovery of novel PETases

et al. 2021

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“…Molecular descriptors that characterize electronic and structural properties of compounds have provided useful predictive information on the toxicity of compounds (Bakire et al, 2018).…”

Section: Synthesis and Characterizationmentioning

confidence: 99%

Synthesis and characterization of polyethylene terephthalate (PET) precursors and potential degradation products: Toxicity study and application in discovery of novel PETases

et al. 2021

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“…As discussed in Section 3.1, the important geometric parameters (the second category) were optimized using a method that applied a MIGA to the RBF neural networks. The RBF neural networks had the universal approximation ability, and they did not suffer from the problem of a local minimum [20]. As shown in Fig.…”

Section: Optimization Of the Important Motor Geometric Parameters Based On The Rbf Neural Network And The Migamentioning

confidence: 99%

Optimization of Torque Ripples in an Interior Permanent Magnet Synchronous Motor Based on the Orthogonal Experimental Method and MIGA and RBF Neural Networks

et al. 2020

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Interior permanent magnet synchronous motors (IPMSMs) have high power densities and speed control performance, and they are widely used in the industry. The problem of reducing the torque ripple of an IPMSM is one of the hot issues in the field of electrical machine design. In order to determine the optimal combination of the geometric parameters to reduce the torque ripple of an IPMSM, a range analysis was conducted on the data from the orthogonal experiments in this study, dividing the rotor geometric parameters into two categories (important and ordinary) based on their degree of impact on the torque ripples of the IPMSM. Thereafter, an optimization of the ordinary parameters was carried out based on the results of the range analysis, whereas the optimization of the important parameters was carried out through a method that combined a multi-island genetic algorithm (MIGA) and Radial Basis Function (RBF) neural networks. The torque ripple of the IPMSM was effectively reduced without materially affecting the output power. Finally, the results of this optimization process were verified using a finite element simulation. The optimization method used in this study divided the motor geometric parameters into two categories and applied a different method of optimization to each parameter type, so it was able to efficiently optimize multiple geometric parameters for the IPMSM.INDEX TERMS Optimization, permanent magnet machines, genetic algorithms, torque.

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“…In silico systems can be employed for ecotoxicological investigations, in which the logarithm of the octanol-water partition coefficient, symbolized in such studies as log K ow , is a principal parameter [73]. Several authors have proposed linear solvation energy relationships (LSER) for ecological risk assessment [71,[74][75][76][77][78][79][80][81]. However, failure in the selection of the appropriate model can result in a 1000-fold error in the estimated indices [82].…”

Section: The Use Of Iam Chromatography For the Prediction Of Ecotoxicitymentioning

confidence: 99%

Immobilized artificial membrane chromatography: from medicinal chemistry to environmental sciences

Tsopelas

Stergiopoulos²,

Tsantili‐Kakoulidou³

2018

ADMET DMPK

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Immobilized Artificial Membrane (IAM) chromatography constitutes a valuable tool for medicinal chemists to prioritize drug candidates in early drug development. The retention on IAM stationary phases encodes lipophilicity, electrostatic and other secondary interactions contrary to traditional octanol-water partitioning. An increasing number of publications in recent years have suggested that IAM indices, including isocratic log k(IAM) or extrapolated log kw(IAM) retention factors, chromatographic hydrophobicity index CHI(IAM) or the polarity parameter Δlog kw(IAM) can successfully model the passage of xeniobiotics through biological membranes and barriers and predict pharmacokinetic properties, often in combination with additional descriptors. Examples referring to the modeling of human oral absorption, blood-brain penetration and skin partition are described. More recently, IAM chromatography has been applied to estimate toxicological endpoints in regard to drug safety, such as phospholipidosis potential, or in regard to chemical risk hazards including the bioconcentration factor and aquatic organisms’ toxicity. The promising results in both medicinal chemistry and in environmental science in combination with the speed, reproducibility and low analyte consumption suggest that a broader application of IAM chromatography in the early drug discovery process and in ecotoxicity may save time and money in initial drug candidate selection and will contribute to a reduced risk hazard of chemicals.

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Developing predictive models for toxicity of organic chemicals to green algae based on mode of action

Cited by 47 publications

References 43 publications

Synthesis and characterization of polyethylene terephthalate (PET) precursors and potential degradation products: Toxicity study and application in discovery of novel PETases

Synthesis and characterization of polyethylene terephthalate (PET) precursors and potential degradation products: Toxicity study and application in discovery of novel PETases

Optimization of Torque Ripples in an Interior Permanent Magnet Synchronous Motor Based on the Orthogonal Experimental Method and MIGA and RBF Neural Networks

Immobilized artificial membrane chromatography: from medicinal chemistry to environmental sciences

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