The main observed qualitative difference between the two networks is in how either handles the alkyl-OH motifs and the (de)halogenations: it appears that in general the LOMAP-Score network allocates more edges to (de)halogenations (e.g. 5k→5m→5i figure ), whereas the RBFENN network focuses more on allocating edges to perturbing the alkyl-OH motifs (e.g. 5o→5p→5i, figure ). This coincides with a recent observation by Cresset developers that the default simulation protocol for SOMD fared poorly for perturbation Fig. RBFE predictions on the TYK2 RBFE benchmarking series versus experimental ligand binding affinities using various RBFE network design methodologies. A-F: predicted ∆G bind versus experimental ∆G bind in kcal•mol -1 for the fully-connected network, and networks generated using the top-performing shallow ML model (random forest with molecular properties), ECFP6 tanimoto similarity on original ligand scaffolds, RBFENN SEM, LOMAP-Score and |∆∆G offset | values, respectively. Shown data is per-ligand relative binding free energy obtained using a weighted least squares approach. Error bars depict statistical uncertainty of each prediction (SEM) and experimental measure. Each plot is annotated with quadrant lines and a 1/2 kcal•mol -1 confidence region (dark gray, gray, resp.). G/H: statistical performance calculated using the data shown in A-F as well as star-shaped and random perturbation networks. In H, the number of edges per network is annotated on each bar. Depicted error bars show the 95% CI of a bootstrapping approach with 10,000 repeats except for RANDOM and Star-shaped statistics where an average and standard deviation is shown (10 random repeats or all 16 possible networks). Fig. Statistical performances of RBFE predictions on the TNKS2 RBFE benchmarking series versus experimental ligand binding affinities using the data-driven approach described in this work (RBFENN; blue) versus the state-of-the-art LOMAP-Score approach (orange) for various statistical metrics. The data is presented as a dynamic representation of replicate inclusion, where for each progression of x all possible combinations of replicates (n=x) are included for the calculation of the mean metric value. Depicted error bars show the standard error of the mean metric across replicates; as for n=5 there is only one combination (all replicates), no confidence has been depicted.