Machine learning guided design of viral vector libraries

Abstract

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.

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