Abstract
Computer-aided molecular design (CAMD) is an efficient product design method that is gradually attracting attention at present. It mainly uses data mining technology to extract information from the existing chemical molecular data and use this information to generate potential excellent molecules. However, the key that CAMD can truly provide accurate and reliable results lies in the efficient utilization of chemical data. In this paper, a series of chemical data analysis methods based on molecular similarity are proposed to enhance the data utilization efficiency of CAMD, which mainly includes 3 applications: adaptive modeling, reliability assessment and advanced data preprocessing including molecular recommendation, data consistency test and data augmentation. We propose specific methodology for each application, and use multiple cases to verify the effect. The results show that molecular similarity can help to improve the accuracy of property prediction at the data level, provide quantification for property prediction reliability, recommend potential excellent molecules with similar structures, locate data mistakes and perform reliable data augmentation, finally enhancing the data utilization efficiency of CAMD.