A strategy that could potentially combine the advantages of both aforementioned approaches is optimizing force fields to match experimental data while minimizing the perturbation to the original ensemble. Latham et al. applied this strategy to optimize a coarse-grained (CG) force field of intrinsically disordered proteins (IDPs) using experimen-tal radius of gyration (Rg) data. Their method starts with learning a biasing potential energy on top of an existing force field using the maximum entropy principle such that simulation results match experiments. The biasing potential energy depends on experimental observables and is system specific. To make the biasing potential energy transferable, they used a transferable correction energy term to augment the original force field and fitted the correction energy term by regressing it against the biasing potential energy. While this technique proved effective, it is limited to augmenting the original force field with additional correction energy terms.