This paper presents a model designed to convert physical molecular models into their digital DeepSMILES notation, aiming to close the gap between physical models and virtual representations. We conducted experiments on various dataset combinations and the effects of beam size, number of images input, and dataset size. We also investigated some interesting properties of our model, such as the model's explainability, through saliency maps and performed error analysis. We also proposed our hypothesis about why beam size did not improve top-1 accuracy significantly, as expected, by examining the model's calibration.