The tool performs de novo design, scaffold hopping, R-group replacement, linker design, and molecule optimization using a Reinforcement Learning algorithm. Users define property profiles as multi-component scoring functions to guide molecule generation. Transfer Learning can pre-train models to bias generation toward specific molecular sets. The system supports GPU acceleration via PyTorch for training and inference.