| 1 |
In silico prediction and analysis of dielectric constant of ionic liquids
Cho CW, Yun YS
Inpress KJChE, 1000(1000), 1, 2022 |
| 2 |
3D-QSAR/HQSAR-based analysis of bioconcentration and molecular modification of monophenyl aromatic compounds
Li Q, Gu WW, Li Y
Turkish Journal of Chemistry, 43(1), 286, 2019 |
| 3 |
Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids
Cao LD, Zhu P, Zhao YS, Zhao JH
Journal of Hazardous Materials, 352, 17, 2018 |
| 4 |
New N-4 piperazinyl derivatives of norfloxacin: design, synthesis, and correlation of calculated physicochemical parameters with antibacterial activity
Abuo-Rahma GE, Abbas S, Shoman M, Samir E, Abdel-Baky R
Turkish Journal of Chemistry, 42(4), 1072, 2018 |
| 5 |
Influence of structure properties on protein-protein interactionsQSAR modeling of changes in diffusion coefficients
Bauer KC, Hammerling F, Kittelmann J, Durr C, Gorlich F, Hubbuch J
Biotechnology and Bioengineering, 114(4), 821, 2017 |
| 6 |
Elimination of trace organic contaminants during enhanced wastewater treatment with horseradish peroxidase/hydrogen peroxide (HRP/H2O2) catalytic process
Na SY, Lee Y
Catalysis Today, 282, 86, 2017 |
| 7 |
A novel model for predicting lower flammability limits using Quantitative Structure Activity Relationship approach
Chen CC, Lai CP, Guo YC
Journal of Loss Prevention in The Process Industries, 49, 240, 2017 |
| 8 |
Application of support vector machine in QSAR study of triazolyl thiophenes as cyclin dependent kinase-5 inhibitors for their anti-alzheimer activity
Garkani-Nejad Z, Ghanbari A
Indian Journal of Chemical Technology, 23(1), 9, 2016 |
| 9 |
A DFT-based toxicity QSAR study of aromatic hydrocarbons to Vibrio fischeri: Consideration of aqueous freely dissolved concentration
Wang Y, Yang XH, Wang JY, Cong Y, Mu JL, Jin F
Journal of Hazardous Materials, 308, 149, 2016 |
| 10 |
PCBs 독성 예측을 위한 주요 분자표현자 선택 기법 및 계산독성학 기반 QSAR 모델 개발
김동우, 이승철, 김민정, 이은지, 유창규
Korean Chemical Engineering Research, 54(5), 621, 2016 |
