Learned representations have been shown to enhance performance, scalability, adaptability, robustness, and generalizability in various applications. The input to 2D graph neural networks (GNNs) for learning the molecular embedding is often a molecular graph. A Simplified Molecular Input Line Entry System (SMILES) string is a convenient way to encode a Lewis structure in a linear text format, making it the standard input for most of the cheminformatics packages and chemical data sets. While Lewis structures are intuitive and easily encoded using SMILES, they only represent local-ized electronic configurations. This limitation becomes apparent in the cases of electron delocalization, where a single Lewis structure cannot fully represent the molecule. The delocalization of electrons across multiple structures, commonly observed in organic chemistry, is defined as resonance or mesomerism. While multiple resonance structures can represent the same molecule, it is a single physical entity with a unique value for each property. In most machine learning packages for property prediction, such as Chemprop, if a different resonance structure of the same molecule is provided as input, it is interpreted as a different molecule, leading to varying predictions. This inconsistency introduces ambiguity for users who must choose which resonance form to pick for representing a species, with each form leading to different predictions. The number of choices is compounded for tasks involving chemical reactions, such as reaction enthalpy or rate-constant prediction, where both reactants and products may exhibit resonance, such as for resonance-stabilized radical reactions common in thermal or oxidative kinetics. One way to implicitly learn resonance invariance is by generating and including all possible resonance structures in the training data set with the same target values. This data augmentation approach significantly increases training costs, but more critically, reliable generation of resonance structures is a complex task. There is no universal method for accurately producing these structures for all molecule types. It often requires manual effort to add custom resonance pathways or modify existing ones, which can be time-consuming and prone to errors. Some 3D GNNs, such as SchNet and PhysNet, which rely solely on atomic coordinates and identities, are invariant to different resonance forms. However, they incur high computational costs and depend on the availability of high-quality 3D data, which can be challenging to obtain for many molecules. As machine learning tools become integral to chemistry, there is a need to develop innovative methods to handle chemical resonance more effectively. To overcome these problems, we present Resonance Invariant Graph Representation (RIGR), a method for featurizing graphs in a resonance-invariant manner. RIGR ensures a consistent molecular representation by generating only the resonance-invariant features or descriptors of the input molecule. While this concept can be applied to most deep learning packages for molecular property prediction, in this work, we focus on Chemprop due to its popularity in the community.