Finally, in the third round, we also decided to explore one more solution to low solubility, conditional variational autoencoder (CVAE). To this end we added solubility scores from SoluProt to the training, discretized into three bins for low, moderate, and high solubility values (Model 4). We conditioned the sampling process from Model 4 using a straight-line evolutionary strategy on the highest bin label forcing CVAE to introduce patterns from sequences with predicted high solubility in decoded designs. We took two variants from Model 4: ancestor 0 (AncDhaA18) with 30 mutations as the control of the pattern extracted from highly soluble sequences and ancestor 18 (AncDhaA19), which had 49 mutations and one deletion in the coil region at position 32 (Fig. ), as it exhibited high confidence in the model (93.28%) and an increased number of high probable residues (85 positions scored above 90%).