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A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/ QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting en-zyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK) , the R2, cross-validation R2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R2cu≥0.82, Q2cu≥0.77, Ermse≤0.44 (GPLS+SVM); ESCK, R2cu≥0.84, Q2cu≥0.82, Ermse≤0.20 (GPLS+INN), respectively.
A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs / QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were The datasets of 58 angiotensin converting en-zyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R2, cross-validation R2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R2cu≥0.82, Q2cu≥0.77, Ermse≤0.44 (GPLS + SVM); ESCK, R2cu≥0.84, Q2cu≥0.82, Ermse≤0.20 (GPLS + INN)