The Junction Tree Variational Autoencoder (JTVAE), pioneered by Jin et. al., represents a significant breakthrough in molecular VAE models. This model innovatively segments the molecular graph into subgraphs, which subsequently become clusters on a junction tree. With this perspective, the JTVAE can decipher almost all meaningful simplified molecular-input lineentry system (SMILES) strings within the limit of its vocabulary set, surpassing previous capabilities of molecular VAEs. However, latent space of VAE is considered as a high-dimensional space. There are two common approaches to handle molecular optimization on this space: Bayesian optimization (BO) and gradient ascent (GA). In earlier studies, the goal property for optimizing drug-likeness is frequently selected as the penalized logP value. Acknowledging the promise of these innovations, our research focused on the exploration of the chemical space, with specific objective of identifying small molecules potentially inhibiting VEGFR-2. This was achieved through the application of local BO and GA methodologies to the JTVAE model. The novel substances identified in our study were anticipated to exhibit key characteristics akin to those of approved VEGFR-2 inhibitors, yet with enhanced properties. To rigorously assess the potential of these newly identified molecules, we established a comprehensive quantitative structure-activity relationship (QSAR) model based on the methodologies developed by Phan et al. alongside a comprehensive molecular docking protocol. 2 Methods