DrugAssist , an interactive LLM designed for molecular optimization tasks. It was benchmarked with two models; a seq2seq model with attention architecture, and a transformer model. Properties tested were Blood-Brain-Barrier Permeability (BBBP) and solubility; their success rate, validity, and average similarity with the pre-optimized molecules. When DrugAssist was compared against traditional approaches; it scored the highest success rates in singleproperty and multi-property optimization and maintained high validity and similarity to the preoptimized molecules. Benchmarks performed poorly; faced difficulty in comprehending the required work; the results offered a guide for users to websites that talk about molecule optimization instead of optimizing molecules. DrugAssist is of good transferability under zeroshot learning. DrugAssist was able to increase BBBP and QED by at least 0.1 simultaneously, resulting with a structurally similar molecule to the pre-optimized one. This means that DrugAssist is able to freely combine and learn individual properties, then to optimize them simultaneously. Moreover, when the model provides a molecule that does not satisfy the requirements, it can correct the error and compensate based on human-provided example, opening a potential to aid researchers in continuous optimization of molecules.