We propose a simple bond-based correction method for reaction energies that can be used for reliable analysis of reaction network energetics with cheap GGA-level DFT functionals. The consistency of the correction terms when using sparsified variables indicate the robustness of this approach. The sparsification, which was not required in our case, further indicates the applicability of this method to datasets with a much higher variety of bonds. The correction method is very effective even for out-of-distribution (from the training set of the linear regression) molecules making it applicable to large, bio-molecular reactions where obtaining benchmark data is challenging. This is interesting because other data-driven approaches such as machine learning models extrapolate poorly. The simplicity of the method makes it easily transferable. We also show that correction coefficients can be obtained from small molecule A ↔ B reactions, and applied to complex multimolecule reactions.