The oral bioavailability of a medicine can be considerably influenced by its
water solubility, which can also have an impact on how the drug is dispersed through
the body. To decrease the likelihood of failures in the late phases of drug development,
aqueous solubility must be taken into account early in the drug research and
development process. By using computer models to predict solubility, combinatorial
libraries might be screened to identify potentially problematic chemicals and exclude
those with insufficient solubility. In addition to predicting solubility from chemical
structure, the explanation of such models can provide insight into correlations between
structure and solubility and can direct structural improvement to improve solubility
while preserving the effectiveness of the medications under study. Such model
development is a difficult procedure that calls for taking into account a wide range of
variables that may affect how well the model performs in the end. In this article,
various solubility modeling techniques are presented. Despite many studies on model
creation, predicting the solubility of various medications remains difficult. One of the
primary reasons for the poor trustworthiness of many of the suggested models is the
quality of the experimental data that may be used to simulate solubility, which is
becoming more widely acknowledged. Consequently, increased availability of
trustworthy data produced using the same experimental technique is necessary to fully
realize the potential of the established modeling tools.
Keywords: Computational tools, Caco-2, Ligand-based computer-aided drug discovery, PAMPA, Permeability, Solubility.