Fig. 3
From: Fast activation maximization for molecular sequence design
Regularized sequence design. a Top: VAE-regularized Fast SeqProp. A variational autoencoder (VAE) is used to control the estimated likelihood of designed sequences during gradient ascent optimization. Bottom: Estimated VAE log likelihood distribution of random sequences (green), test sequences from the MPRA-DragoNN dataset (orange) and designed sequences (red), using Fast SeqProp without and with VAE regularization (top and bottom histogram respectively). b Oracle fitness score trajectories (APARENT, MPRA-DragoNN and Optimus 5’) and validation model score trajectories (DeeReCT-APA, iEnhancer-2L and retrained Optimus 5’) as a function of the cumulative number of predictor calls made during the sequence design phase. Shown are the median scores across 10 samples per design method, for three repeats. c Example designed sequences for APARENT, MPRA-DragoNN and Optimus 5’, using Fast SeqProp with and without VAE-regularization. Oracle and validation model scores are annotated on the right