Jihui Tang*, Jie Ning, Xiaoyan Liu, Baoming Wu and Rongfeng Hu* Pages 206 - 211 ( 6 )
Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates.
Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening.
Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%.
Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.
Cell-penetrating peptides, machine learning, prediction, support vector machine, IBM spss modeler, amino acid position.
School of Pharmacy, Anhui Medical University, 81 Meishan Road, Hefei 230032, Department of Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei 230022, School of Pharmacy, Anhui Medical University, 81 Meishan Road, Hefei 230032, School of Pharmacy, Anhui Medical University, 81 Meishan Road, Hefei 230032, Key Laboratory of Xin’an Medicine, Ministry of Education, Anhui Province Key Laboratory of R&D of Chinese Medicine, Anhui University of Chinese Medicine, Anhui