The JTVAE was implemented with a neural network that predicts the molecules' properties, as stated in the original paper, and contributes as a critical component for facilitating constrained optimization using the GA technique (Fig. ). The auxiliary network incorporates a forward structure, initiated by a latent vector serving as the input. This is followed by a linear layer that outputs a 450-dimensional vector. Subsequently, a tanh activation layer processes this output, culminating in a predicted property value at the network's output. The output value is then compared with the original properties value to compute the loss function of the model based on mean squared error (MSE).