Algorithm-empowered DNA molecular machines

DNA molecular machines (DMMs) have evolved from simple responsive devices into nanosystems capable of performing complex biochemical tasks. By mapping algorithmic rules onto DNA hybridization kinetics and structural dynamics, algorithm-empowered DMMs exhibit autonomous and self-regulated behaviors beyond passive and repeated operations. This perspective highlights the recent advances in the application of algorithm-empowered DMMs in biomedical science and information science. In biomedical science, pattern recognition and thresholding algorithms enable DMMs to execute spatially matched molecular recognition and concentration-dependent activation, offering high specificity and adaptive therapeutic control. In information science, depth-first search and node partition algorithms implemented through DNA origami and strand displacement reactions demonstrate nanoscale computation, pathfinding, and graph traversal. The perspective concludes by discussing challenges in clinical translation, scalability, and kinetic control, emphasizing future directions toward intelligent molecular systems that integrate biochemical computation and information science.