Random forest regression models were trained on 12 orientational features to learn the relationship between conformation and binding affinity (Supporting Information Table ). These models were implemented using Scikit-learn 103 v. Initial and optimized 3D structures of all native and synthetic close contacts; EDIAm scores, raw electronic energies and interaction energies from DFT, and SAPT0 total and component energies for the full dataset of close contacts; interaction energies of the interaction in the benchmarking dataset, as determined by DLPNO-CCSD(T), MP2, DFT, SAPT0 and SAPT2; random forest models trained to predict the DFT interaction energy, SAPT0 interaction energy, dispersion, electrostatics, exchange, and induction energies (ZIP)