Next, we present the different computational methods, both semi-empirical and data-driven that we explore for predicting toluene/water partition coefficients. We choose COSMO-RS, a physics-based model, to generate low fidelity data because it performs better than the other available methods like GC and MD. Based on this low fidelity data, we develop several multi-fidelity ML approaches to address the issue of limited high fidelity experimental data.