Protein sequences containing other than the 20 natural amino acids were not considered for modelling with PCM. The collected negative logarithmically scaled bioactivities values were modelled using the XGBoost (version 1.7.5) implementation of gradient-boosted regression trees . Molecules were represented with the 777 physicochemical and topological Mold2 molecular descriptors . Unaligned protein sequences were described with ProDEC 75 by splitting them into 50 equal parts and averaging the first three principal components (PCs) of Sandberg et al.'s amino acid descriptors over each part and over the entire sequence for each of the three PCs, resulting in 153 features (50 parts x 3 PCs + 3 averages PCs) . Models were 5-fold cross-validated using a random split with a random seed set to 1234 and using a leave-one-out strategy applied for each sequence variant (LOVO). Accessions with less than five data points were disregarded for QSAR modelling and data points related to only one variant were not considered for PCM modelling. Applicability domains were derived using MLChemAD (version 1.2.0) with isolation forests by fitting the training subsets and evaluating them on the Enamine Hit Locator Library (downloaded on 24/01/2024), emulating a typical real-world virtual screening. Finally, the performances of cross-validated models were statistically evaluated between 'PCM-All', 'QSAR-WT`, and 'QSAR-All` models using Friedman's test for repeated samples using Scipy (version 1.11.2). Significant differences (p-value<0.05) were further investigated using pairwise uncorrected post-hoc Conover-Friedman tests (p-value<0.05) using scikit_posthocs (version 0.8.0).