In the pharmaceutical and medicinal industries, the partition coefficient (P), which represents the ratio of the concentration of a solute between two immiscible phases in an un-ionized state, is an extremely important physical parameter. This coefficient, usually between octanol-water (logPo/w), is a tool applied in lead candidate optimization. Lipinski's Rule of 5, 1 aimed at identifying orally viable drug candidates, states an ideal candidate will have a logPo/w < 5. While there are clear classes of drugs outside the parameters set by the rule of 5, 2 these guidelines are an excellent starting place to help locate molecules that strike the balance between solubility and permeability. In this context, rational drug design based on thermodynamic properties is critical in their development and success as drug candidates, since only about 10% of drug candidates that enter Phase 1 clinical trials are ultimately approved by the U.S. Food and Drug Administration. Computational efforts are often a first step in screening potential drug candidates by utilizing bio-and cheminformatic toolkits that consider various molecular properties. The impact of computational chemistry on drug discovery has intensified because of the rapid development of faster architectures and more computationally efficient algorithms and effective methods for high-level computations. However, these computational approaches have drawbacks relating to the complex nature of the chemical space and how effectively current computing architecture can handle the exploration of the vast chemical space. Therefore, to expedite drug screening efforts and reduce trial-and-error drug synthesis, computational methods need to achieve a level of accuracy and reliability when predicting thermodynamic properties where the experimental values have not been generated.