SlowGuess/NeurIPS_2023_OCR huggingface dataset:
(B)
Figure 7: Distribution of the latent embeddings with (A) and without (B) Wasserstein loss. Orange lines correspond to dimensions of  $\mathbf{z}_f$  and green lines  $\mathbf{z}_f$ . The distribution is estimated using gaussian_kde from the scipy package.
(A)
$z_{s}$  of positive sequences
Input embedding of positive sequences
(C)
(B)
(D)
(E)
Correlation between validity score and binding score (F)
Interpolation of  $z_{f}$
Figure 8: (A) T-SNE of  $\mathbf{z}_s$  (left) and first layer embedding of the encoder (right) of positive TCRs, colored by their binding peptides. (B) The average binding score of generated positive and negative TCRs. (C) The length distribution of template and optimized TCRs (CDR3 $\beta$  region) from VDJDB. (D) Cosine similarity between  $\mathbf{z}_s$  of the optimized sequences vs their templates (left),  $\mathbf{z}_f$  of the optimized sequences vs their templates (middle),  $\mathbf{z}_f$  of the optimized sequences vs the modified  $\mathbf{z}_f$  (right). (E) Scatter plot between the validity score and binding score of the engineered sequences (for simplicity, only 500 out of 5000 points are shown). (F) Binding score of interpolated samples between positive and negative pairs.