To generate the Low fidelity -Quantum chemical (LF-QC) dataset of log P values, we initially collect molecules represented by SMILES strings from the iBonD database , covering a diverse range of chemical classes and atom types. The iBonD database is chosen because it contains many drug-like molecules similar to those in the experimental datasets investigated in this work while also covering a broad spectrum of chemical diversity. The molecules are selected on the basis of standard ranges of acid dissociation constants. The final selection consists of molecules, predominantly featuring substituted benzoic and phenolic acids, alkyl carboxylic acids, alkylamines, and derivatives of pyridine and aniline. We then use these SMILES strings as input to obtain the 3D geometric structures using the software RDKit . Next, we optimize the molecular structures obtained from RDKit at the GFN2-xTB level of theory . We further refine the geometries of each molecule in the COSMO state using COSMOconf 23 (Dassault Systèmes, 2023a), with the BP86/TZVPD parametrization and FINE COSMO cavity . Finally, we calculate the log P tol/w values for each molecule at 25 °C and at low finite dilution (0.0002 mol%) using COSMOtherm 23 (Dassault Systèmes, 2023b), based on the difference in chemical potential between the water and toluene phases. We utilize small finite fractions of the molecules in both the aqueous phase and toluene to match the solute concentration range used in the experimental studies, which is 2.0-0.5 mM . The error of log P in the LF-QC dataset is determined by propagating the uncertainties of the solvation free energies in water and toluene using Equation .