From data heterogeneity aspect, a critical issue is the comparability and uncertainty of bioassay endpoints with different activity types, which can potentially limit the effectiveness of data-driven CPI methods in practical virtual screening. For instance, IC 50 values are influenced by the reaction types, concentrations of enzymes, inhibitors, substrates, and other experimental conditions, while K i values are intrinsic thermodynamic quantities that depend solely on the interactions between enzymes and inhibitors. It is generally accepted that thermodynamic affinity properties, such as K i and K d , offer the highest data quality and consistency. In contrast, other bioactivity measures like EC 50 , IC 50 , and percentage inhibition tend to be less reliable. Given these inconsistencies, although directly convert all bioactivity values into identitcal types is impractical, such highly-correlated bioactivity values cross different types may still benefit for learning a robust affinity predictor given the superior power of ML models in fitting linear relationships. However, blindly combining bioactivity data from different assays could introduce significant noise, thereby impairing the performance of data-driven CPI models. A recent study has also demonstrated that careful data integration and multistage machine learning modeling can successfully leverage multiple data types to improve kinase bioactivity prediction. These evidences highlight the need for comprehensive data processing and tailored CPI modeling strategies to effectively utilize large-scale, but often noisy, bioactivity data.