The design and optimization of compounds towards potential drug candidates is crucial in drug discovery. The main challenges include the large chemical search space and the requirement of optimization towards multiple properties e.g. physicochemical properties, safety, synthetic feasibility and potency against its target. To accelerate the molecular design and optimization process, various deep neural networks have been explored as molecular generative models, e.g. recurrent neural networks (RNNs) , variational autoencoders (VAEs) , transformers , generative adversarial networks (GANs) , graph neural networks (GNNs) and diffusion-based models . Early work have been focusing on de novo molecular design which generates molecules from scratch without needing a starting compound, while there is an increasing attention on conditional compound generation and optimization from a specific starting structure that shows promise, e.g. compounds , scaffolds and fragments . In this work, we focus on compounds as starting point. In previous publications , we treated the molecular optimization problem as machine translation task and trained the transformer model on pairs of similar molecules extracted based on different similarity criteria e.g. Tanimoto similarity on fingerprint, matched molecular pairs and scaffold. The model learns to generate similar molecules to a given input molecule. To generate compounds with desired properties, property change tokens are prepended to the simplified molecular-input line-entry system (SMILES) tokens in order to steer the model towards the chemical space of interest. However, this model is limited to the preselected set of properties during optimization.