1. For each decryptor we calculate all 788 time series descriptors (ts_descriptors) supported by tsfresh (see Figure ) . 2. For each ts_descriptor, its p-value is determined in terms of statistical significance against experimentally determined affinity (sourced from PDBBind), using a univariate test with FDR set to 0.001. 3. Next, a trimming procedure is used. For each of the 75 features types , the ts_descriptor with the lowest p-value is selected. Some ts_descriptors are parametric in nature; for such cases we use different thresholds to generate several versions of that ts_descriptor and select the one with the lowest p-value. At this stage, there could be a maximum of 75 ts_descriptors per a single descriptor. 4. To avoid caveats in training, trimming of correlations was applied. All descriptors and ts_descriptors were tested for correlations with each other. Correlated descriptors were dropped if PCC was >= 0.8. Among a group of correlated ts_descriptors, the one with the highest cardinality was left. 5. In the final filtering step, for each descriptor, the ts_descriptor with the highest mutual information score between itself and the experimentally determined affinity value was selected. As a result, each descriptor is described with at most one ts_descriptor (Figure , bottom left). In total, each crystallographic complex is described by 63 descriptors. For the MD data representation the same complex is described by a maximum of 51 ts_descriptors and 19 ligand property (static) descriptors, for which time series cannot be generated. Both representations are augmented with the ECFP4 fingerprint (1024bits) to directly incorporate ligands molecular connectivity information. light green -crystallographic (static) descriptors, light blue -multiframe MD descriptors and ts_descriptors, light orange -ligand descriptors. See section "Representation" for more details.