Toward the Engineering of Chameleonicity: Quantum Mechanical Prediction for the Octanol/Water Distributions of Large...

This study predicts octanol/water partition and distribution coefficients (logP and logD) for 18 triazine macrocycles, which serve as indicators of how drug-like compounds partition between lipid and aqueous environments. Using DFT-computed solvation free energies (ωB97X-D/6-311++G(2d,p)/6-31G(d)/SMD), we compare predicted values with experimentally measured logD. Our approach models solvent-dependent conformations and ensemble distributions for a family of 24-atom triazine macrocycles exhibiting well-defined hinge motions. Because experimental determination of these properties is time-consuming and costly, reliable computational predictions are essential. Simple additive models (AlogP) fail to accurately capture the behavior of these macrocycles. In contrast, their well-defined structures and conformational flexibility make them promising candidates for therapeutic development. Incorporating intramolecular hydrogen bonding (IMHB) and environment-dependent conformational changes into computational models enables simultaneous optimization of solubility and permeability. Our quantum mechanical method, combined with a linear correction, predicts logP and logD for macrocycle A with root-mean-square deviations of 0.9 and 0.8 log units, respectively, slightly outperforming AlogP. Careful consideration of conformational dynamics, protonation states, polarity, and IMHB significantly improves prediction accuracy. However, AlogP remains an effective high-throughput screening metric due to its minimal computational cost.