Molecular optimization. Several methods have been proposed for optimization of molecular generators for drug design applications. Early approaches utilized generative learning in which a generator (usually a GAN or VAE) is coupled to a property predictor and the resulting optimization problem is solved using Bayesian optimization. Reinforcement learningbased optimization models employ a similar workflow utilizing policy optimization algorithms to produce high-quality molecules. More recently, Jin et al. introduced a graph-tograph translation model based on matched molecular pair analysis which learns to translate a molecular graph to a better molecular graph. Further, Zang and Wang train a multi-layer perceptron to regress the latent representations of the molecules against the desired properties (penalized logP and QED), searching for the best-performing molecules through gradient ascent.