To further challenge and highlight the generality of our model, we applied it to predict DNA strand displacement rate constants. To construct training data, a strand displacement pool including 10,000 sequence sets was established (Figure ). By using the X-probe architecture we only consider sequences participating in the reaction when designing the sequence set. The length of the toehold domain was designed ranging from 4 to 14 nucleotides. The spacer domain was designed as 0 to X nucleotide T, where X is not greater than 10 and less than the length of the toehold domain. The length of the branch migration domain was designed to be 22 nucleotides. Each sequence set has an invading strand, a substrate complex and a product complex. The invading strand consists of a toehold domain (t), a spacer domain (s1) and a branch migration domain (b). The top strand in the substrate complex is the branch migration domain (b), whereas the bottom strand in the substrate complex consists of complementary toehold domain (t*), another spacer domain (s2) and complementary branch migration domain (b*). The sequence of the product complex was obtained by combining the above sequences. To ensure strand displacement reactions for all sequence sets, Nupack was used to determine whether the toehold domain in the product complex can be stably combined, if not, a new sequence set will be generated and added into the pool. The secondary structure of each sequence is determined by the minimum free energy structure calculated by Nupack, which may not be entirely accurate as it only reflects the most probable structure. Therefore, a row was appended to the descriptor matrices to store the Nupack-computed unpaired probability of each nucleotide at equilibrium. The values corresponding to the second initiation in each stack, as well as those associated with the initiation of the nick, were set to zero. Additionally, rate constants of strand displacement with same sequences varies with temperatures, another row was added to the descriptor matrices to represent the temperature with values normalized. The longest DNA strings of the invading strand and the product complex were 62 and 101 characters respectively, thus zeropadding was applied to all descriptor matrices. The two matrices were input into the model, the convolution kernel size used in the model was 5×4, and other hyperparameters remained unchanged. They were combined into a one-dimensional vector after convolution, and were subsequently fed into the feed-forward neural network (Figure ). Fluorescence kinetic characterization analysis was used as the labeling method of the pool, which measured time kinetic traces of reactions by the fluorescence spectrophotometer (Figure ) and determined strand displacement rate constants by fitting analysis.