Interpreting an infrared spectrum requires expert knowledge to correlate the spectrum with the substructures of the molecules and to account for the relative environment and molecular structure. However, this knowledge-based manual approach can be challenging to formalize and implement, specifically when aiming to apply it to complex systems with overlapping absorption signals from different chemical species. Therefore, the classical (manual) analysis of IR data is resource-and time-consuming, allowing only a low throughput of analytical investigations. These limitations hamper the interpretation of complex IR spectra and the systematic and fast interpretation needed for high-throughput analysis of samples by IR spectroscopy. Recently, computational methods have been playing an increasing role in analyzing IR spectra, bringing together advancements in machine learning and quantum chemistry. These methods help in accurately predicting and interpreting intricate IR spectra, making it easier to identify molecular structures. Traditional computational methods include e.g. the option to implement efficient forward spectra prediction models and the potential to integrate with extensive databases. Key advantages of using computational methods compared to manual analysis are the increase in speed, the increase in accuracy and reproducibility. Nevertheless, also traditional computational techniques have their limitations which are e.g. the dependency of handcrafted descriptors based on expert knowledge to extract relevant spectral signals.