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Directed evolution, with iterated mutation and human-designed selection, is a powerful approach for drug discovery. Here, we establish a fitness inference problem given on-target and off-target time series DNA sequencing data. We describe maximum likelihood solutions for the nonlinear dynamical system induced by fitness-based competition. Our approach learns from multiple time series rounds in a principled manner, in contrast to prior work focused on two-round enrichment prediction. While fitness inference does not require deep learning in principle, we show that inferring fitness while jointly learning a sequence-to-fitness transformer (DeepFitness) improves performance over a non-deep baseline, and a two-round enrichment baseline. Finally, we highlight how DeepFitness can improve the diversity of the discovered hits in a directed evolution experiment.
Author Information
Nathaniel Diamant (genentech)
Ziqing Lu
Christina Helmling
Kangway Chuang (Genentech Research and Early Development)
Christian Cunningham (University of California, San Francisco)
Tommaso Biancalani (Genentech)
Gabriele Scalia (Genentech)
Max Shen (Genentech)
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