Fig. 4: Evaluation of neural network architectures with increased capacity.
Performance metrics for convolutional neural networks (CNN) and long short-term memory (LSTM) networks trained on one-hot encoded toehold sequences, as well as a CNN trained on a two-dimensional, one-hot encoded sequence complementarity map. All models are compared to the previously reported MLPs trained on the 30 pre-calculated thermodynamic features and one-hot toehold sequences. For regression-based predictions, a shows box-and-whisker plots for R2 metric, while b shows box-and-whisker plots for mean absolute error (MAE) for all models. In the case of classification-based predictions, c shows box-and-whisker plots of the area under the curve (AUC) of the receiver–operator curve (ROC) and the precision-recall curve (P–R) for all tested models. In both regression and classification, the one-hot encoded sequence MLP delivered a top-in-class performance as compared to higher-capacity deep-learning models. d ROC curves of pre-trained higher-capacity classification models validated with an unseen 168-sequence external dataset from Green et al.2. For all box-and-whisker plots, the horizontal line indicates the median, box edges are at the 25th and 75th percentiles, and whiskers indicate the smaller of either 1.5 × IQR or max/min. All source data are provided as a Source Data file.
