In this study, we present a method to predict of the presence of functional groups in chemical compounds based on the measured IR spectroscopic data. Our method uses IR data as the only type of measured input which makes it better and broader usable due to the independence of other data types. Our method 'Own NIST' uses a deep learning model that performs better than previous work that used a combination of IR spectrum and mass spectrum inputs. At the same time, our model significantly improves the classification accuracy of nitriles and amides groups. Our method yields predictions that perfectly match 70% of the molecules in the validation set. Besides the provision of the model itself, we demonstrated the integration of open access data available from a research data repository. We were able to show that, despite the fact that the open-access data is in-homogeneous, comparable results could be obtained.