QSAR Study of Angiotensin I-Converting Enzyme Inhibitory Peptides Based on Different Modeling Methods
  
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DOI:10.3969/j.issn.1004-4957.年份.月份
KeyWord:angiotensin I-converting enzyme inhibitory peptides  quantitative structure activity relationship  multiple linear regression  partial least square regression  artificial neural networks
  
AuthorInstitution
苏淅娜,管骁,刘静 1.上海理工大学医疗器械与食品学院;2.上海海事大学信息工程学院
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Abstract:
      A new ACE inhibitory peptides database was self-established.After the structures of peptide samples with different lengths were characterized using amino acid descriptors SVHEHS,the data obtained were treated for standardization by auto cross covariances(ACC).Then three modeling methods,namely multiple linear regression(MLR),partial least squares(PLS) and artificial neural network(ANN) were used to establish the models of the QSAR of ACE inhibitory peptides,respectively.The results showed that R2(correlation coefficient) of MLR,PLS and ANN models were 0.744,0.862 and 0.958,Q2LOO(leave-one-out cross-validated correlation coefficient) were 0.532,0.829 and 0.948,and Q2ext(external validated correlation coefficient) were 0.567,0.632 and 0.634,respectively.Hence,the combinations of SVHEHS and the above three modeling approaches were all useful for the QSAR of ACE inhibitory peptides,in which ANN modeling approach is the best.
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