DNAzyme Design Using Machine Learning Models

The patent applies preliminary action by using in silico computational methods to predict and evaluate DNAzyme sequences before laboratory testing. The system performs virtual screening of potential DNAzyme candidates using algorithms that assess catalytic activity, specificity, and stability, allowing researchers to pre-select the most promising sequences for experimental validation. This preliminary computational assessment significantly reduces the number of sequences that require time-consuming wet lab testing.

The patent employs copying by creating virtual models and simulations of DNAzyme behavior in silico. Instead of physically testing every possible DNAzyme sequence in the laboratory, the system creates computational copies that simulate catalytic activity, binding affinity, and structural stability. These virtual models allow for rapid evaluation of thousands of sequences without consuming physical materials or experimental time, thereby accelerating the design process while maintaining reliability.

The patent applies mechanics substitution by replacing the mechanical wet lab trial-and-error process with computational in silico methods. Instead of physically synthesizing and testing numerous DNAzyme sequences in the laboratory, the system uses algorithms and computational models to predict sequence functionality. This substitution of computational mechanics for physical experimentation dramatically reduces costs associated with reagents, laboratory equipment, and manual labor while maintaining the ability to identify functional DNAzymes.

The patent implements self-service by enabling the computational system to automatically evaluate and select optimal DNAzyme sequences without requiring extensive manual intervention. The in silico platform autonomously performs sequence analysis, predicts catalytic activity, assesses specificity, and ranks candidates, reducing the need for researcher time and laboratory resources. This automated self-evaluation process makes the design process more cost-effective while maintaining high standards of functionality.