Machine learning based predictive analysis of DNA cleavage induced by diverse nanomaterials

Abstract

DNA cleavage by nanomaterials has the potential to be utilized as an innovative tool for gene editing. Numerous nanomaterials exhibiting DNA cleavage properties have been identified and cataloged. Yet, the exploitation of property data through data-driven machine-learning approaches remains unexplored. A database was developed, compiling thirty distinctive characteristics, encompassing physical and chemical properties, as well as experimental conditions of nanomaterials that have demonstrated DNA cleavage capability such as in articles published over the past two decades. The DNA cleavage effect and efficiency of nanomaterials were predicted using machine learning algorithms such as support vector machines, deep neural networks, and random forest, and a classification accuracy of 0.93 for the cleavage effect was achieved. Moreover, the potential of utilizing larger datasets to enhance the predictive capacity of models was discussed. The findings indicate the feasibility of predicting nanomaterial properties based on experimental data. Evaluating the performance and effectiveness of the machine learning models trained using the existing data can furnish valuable insights for future materials research endeavors, especially for the design of DNA cleavage with specific sites.