A Machine Learning Framework for In Silico Screening of AAV Capsid Libraries Using Protein Language Models
Adeno-associated viruses (AAVs) are among the most promising vectors for safe and effective gene delivery in therapeutic applications. However, engineering functional AAV capsids remains a major challenge due to the vast sequence space, unpredictable biological performance, and limited translational success of many variants. Machine learning offers a powerful strategy to address these limitations
