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

Computational approaches, including machine learning, are increasingly enabling the design of viral vectors for gene therapy, advancing the field significantly.

• Why it matters: Optimized viral vectors are crucial for effective gene therapy, but designing variants with desired properties remains complex; computational methods aim to address this challenge.
• What they did: The review synthesizes recent advances in computational viral vector design, focusing on algorithmic innovations, experimental integration, and key factors influencing success.
• The result: These developments highlight the transformative potential of computational design, encouraging broader participation and shaping the future of viral gene therapy strategies.