Although the prediction quality is overall worse, the composite prediction method has the benefit of modularity. The models could be switched out for more accurate machine learning models developed in the future, or even with QM calculations if a higher level of accuracy is desired. There are numerous combinations of models for different properties that can be chosen, which future work should investigate further. In particular, using an accurate solvation method to compute ∆G * solv (AH) with the other ML models might result in model performance comparable or better than the direct method. We therefore see potential use cases for both the direct and composite prediction methods.