- 作者: 劉樹深
- 作者服務機構: State Key Laboratory of Pollution Control and Resources Reuse, Department of Environmental Science & Engineering, Nanjing University, Nanjing 210093, P. R. China
- 中文摘要: A combinatorial method for estimating and predicting the biological activities of two sets of dipeptides, a set of 48 compounds and another set of 58, was developed. The molecular holographic distance vector (MHDV) was employed to characterize the structures of the peptide molecules. Preliminary selection of the MHDV descriptors was performed based on the number of the molecules having non-zero MHDV values. The final optimal descriptors were completed by a genetic algorithm-based variable selection procedure. Then the optimal descriptors are used to relate to the biological activities of the peptides using the multiple linear regression (MLR) method. For two panels of dipeptides, the correlation coefficient of estimations R are respectively 0.9651 for 48 peptides and 0.936 for 58 peptides, and the correlation coefficient of leave-one-out predictions (q) are respectively 0.9452 and 0.9075.
- 英文摘要: --
- 中文關鍵字: Molecular Holographic Distance Vector (MHDV); Genetic Algorithm (GA); Quantitative Structure-Activity Relationship (QSAR); Dipeptides; Multiple Linear Regression (MLR)
- 英文關鍵字: Molecular Holographic Distance Vector (MHDV); Genetic Algorithm (GA); Quantitative Structure-Activity Relationship (QSAR); Dipeptides; Multiple Linear Regression (MLR)