Du Qi-Shi, Wang Shu-Qing, Xie Neng-Zhong, Wang Qing-Yan, Huang Ri-Bo, Chou Kuo-Chen
State Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China.
Gordon Life Science Institute, Boston, MA 02478, USA.
Oncotarget. 2017 Aug 1;8(41):70564-70578. doi: 10.18632/oncotarget.19757. eCollection 2017 Sep 19.
A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.
基于主成分分析(PCA)方法提出了一种两级主成分预测器(2L-PCA)。它可用于定量分析各种化合物和肽的功能或成为有用药物的潜力。一个级别用于处理药物分子的物理化学性质,而另一个级别用于处理其结构片段。该预测器具有自学习和反馈功能,可自动提高其准确性。预计2L-PCA将成为在药物开发过程中及时提供各种有用线索的非常有用的工具。