Machine-guided design for bioengineering gene therapy vectors: Where are we and what lies ahead?

Computational design methods have enabled the creation of optimized viral vectors, advancing gene therapy with significant improvements in vector properties.

• Why it matters: Optimized viral vectors are crucial for effective gene therapy, but designing them is complex and limited by traditional methods, creating a need for more systematic approaches.
• What they did: The review synthesizes recent advances in machine learning and non-machine-learning computational techniques used to engineer viral vectors, focusing on stability, structure, and function prediction.
• The result: These computational strategies have the potential to revolutionize viral vector bioengineering, making gene therapies more effective and accessible, and encouraging broader participation in this innovation.