  Finely-tuned enzymatic pathways control cellular processes, and their
dysregulation can lead to disease. Creating predictive and interpretable models
for these pathways is challenging because of the complexity of the pathways and
of the cellular and genomic contexts. Here we introduce Elektrum, a deep
learning framework which addresses these challenges with data-driven and
biophysically interpretable models for determining the kinetics of biochemical
systems. First, it uses in vitro kinetic assays to rapidly hypothesize an
ensemble of high-quality Kinetically Interpretable Neural Networks (KINNs) that
predict reaction rates. It then employs a novel transfer learning step, where
the KINNs are inserted as intermediary layers into deeper convolutional neural
networks, fine-tuning the predictions for reaction-dependent in vivo outcomes.
Elektrum makes effective use of the limited, but clean in vitro data and the
complex, yet plentiful in vivo data that captures cellular context. We apply
Elektrum to predict CRISPR-Cas9 off-target editing probabilities and
demonstrate that Elektrum achieves state-of-the-art performance, regularizes
neural network architectures, and maintains physical interpretability.
