CZ Biohub SF, LLC (20240404640). MACHINE LEARNING GUIDED DESIGN OF VIRAL VECTOR LIBRARIES
MACHINE LEARNING GUIDED DESIGN OF VIRAL VECTOR LIBRARIES
Organization Name
Inventor(s)
David V. Schaffer of Danville CA (US)
Jennifer Listgarten of Berekeley CA (US)
DanQing Zhu of Oakland CA (US)
David Henry Brookes of Easton MD (US)
MACHINE LEARNING GUIDED DESIGN OF VIRAL VECTOR LIBRARIES
This abstract first appeared for US patent application 20240404640 titled 'MACHINE LEARNING GUIDED DESIGN OF VIRAL VECTOR LIBRARIES
Original Abstract Submitted
various methods and systems are provided for designing viral vector libraries using machine learning models. in some embodiments, by training a machine learning model to predict a packaging fitness of a viral vector sequence, a viral vector library may be designed, wherein, for a desired library diversity, an increased packaging fitness may be achieved. in one example, a machine learning model may be trained to predict packaging fitness of a viral vector sequence by encoding the viral vector sequence as a feature set, mapping the feature set to a predicted packaging fitness of the viral vector sequence using a machine learning model, determining a loss based on a difference between a ground truth packaging fitness and the predicted packaging fitness of the viral vector sequence, and updating parameters of the machine learning model based on the loss.
