Machine learning-driven molecular engineering of nucleic acids.

Area of Science:

• Biomedical engineering
• Molecular biology
• Computational biology

Background:

• Molecular engineering drives advancements in gene therapy, disease diagnosis, and biosensing.
• Nucleic acid engineering faces challenges in design space, structure-function prediction, and optimization.
• Current methods are often empirically driven, leading to lengthy validation cycles.

Purpose of the Study:

• To systematically review recent progress in machine learning (ML) applications for nucleic acid molecular engineering.
• To explore ML's potential in constructing predictive models for sequence-structure-function relationships.
• To identify core challenges and potential solutions in ML-driven nucleic acid engineering.

Main Methods:

• Systematic literature review of ML applications in nucleic acid engineering.
• Analysis of ML's role in structure construction, performance modulation, and application expansion.
• Discussion of challenges including data quality, model interpretability, and experimental validation.

Main Results:

• ML enables data-driven approaches, shifting from empirical methods to predictive modeling.
• ML applications span nucleic acid structure construction, performance modulation, and diverse applications.
• Key challenges include data quality, model interpretability, and efficient experimental validation.

Conclusions:

• ML facilitates a paradigm shift in nucleic acid engineering towards dynamic behavior simulation and complex system design.
• Future directions include hybrid ML-quantum models and applications to non-canonical nucleic acids.
• These advancements promise transformative innovation in biomedicine, environmental monitoring, and information technology.