Abstract
Melting point is a basic physical property that specifies the transition temperature between solid and liquid phases. Melting point has numerous applications in biochemical and environmental sciences due to its relationship with solubility. Sufficient aqueous solubility is essential for a compound to be transferred to the site of action within an organism. In spite of the huge number of available melting point data, few useful guidelines exist for understanding the relationship between the compound melting point and its chemical structure. Therefore, methods for estimating the melting point of organic compounds would considerably help medicinal chemists in designing new drugs within a specified range of melting point and solubility. A highly effective tool depending on quantitative structure–property relationship (QSPR) can be utilized to predict melting point for drug-like compounds with no literature values. QSPR models were developed using genetic algorithms, multiple linear regression and neural networks analyses. Predictive non-linear QSPR models were developed for the relevant descriptors. The results obtained offers excellent regression models that possesses good prediction ability.
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