This study investigates the informativeness of protein representations derived from high-quality, structure-guided MSA for proteochemometric models, with a rigorous evaluation of embedding quality and overall model performance. Human kinases serve as the case study since kinase inhibitors represent a major class of FDA-approved therapeutics targeting a wide range of diseases, such as leukemia, breast cancer, non-small cell lung cancer, renal cell carcinoma , as well as various neural and metabolic disorders . Many ML/DL SFs have also been trained for the purpose of kinase drug discovery , using the dedicated Davis , KIBA or KLIFS datasets. Therefore, kinases were a natural choice for a deeper investigation of protein representation utility.