To test the hypothesis that time series descriptors improve affinity prediction, we first assess the difficulty of predicting the affinity of MDD complexes using published models with publicly available code and training procedures . Selected models were trained on the PDBBind dataset (v2016) with 862 MDD complexes excluded (Table ). We compare these results against those obtained for CASF2016 dataset presented in the literature (Table ). Both PCC and RMSE values (Table ) render the MDD as a more difficult dataset compared to CASF2016 (Table ). All tested models show a rather consistent drop in performance, independent of their complexity. The observed decrease in performance may be multifaceted: CASF2016 is a relatively small dataset compared to MDD (285 vs 862 complexes) therefore it may be easier to optimize or overtrain the models. Also , excluding MDD complexes from the training data may have influenced the availability of information necessary for higher affinity prediction performance. Our descriptor model (see Representation section in Materials and Methods) shows the same consistent performance drop as seen with other models. Interestingly, our models' affinity prediction performance is on par with some of the best, highly sophisticated methods. This result highlights that a carefully selected set of descriptors and relatively simple ML model can show a level of performance comparable to specialized neural networks.