Çoklu Doğrusal Regresyon Kullanılarak Monokarboksilik Asitlerin Bazı Termodinamik Özelliklerin Belirlenmesi

Topolojik indeksler, moleküler G grafı aracılığıyla elde edilmiş moleküler yapının nümeriksel tanımlayıcısıdır.Topolojik indeksler, yapı-özellik ilişkisi (QSPR), yapı-aktivite ilişkisi (QSAR) ve kimya, nanoteknoloji vefarmakolojideki yapısal tasarım gibi moleküllerin özelliklerini incelemek için kullanılır. Başlıca görevi QSARQSPR modellerinde sayısal bir moleküler tanımlayıcı olarak çalışmaktır. Üstelik kaynama noktası, buharlaşma vekararlılık entalpisi gibi fizikokimyasal özellikler, QSAR/QSPR modelleri ile öngörülebilir. Bu çalışmada, tekkarbonlu asitlerin termodinamik özelliklerini tahmin etmek için Forgotten topolojik indeks, Forgotten coindex,max-min Rodeg indeksi ve ters toplam indeg indeksi kullanılarak QSPR (Nicel Yapı - Özellik İlişkisi) modelitasarlanmıştır. Termodinamik özellikler ile topolojik indeksler arasındaki ilişki analizi çoklu doğrusal regresyonyöntemi kullanılarak yapıldı.

Determination of Some Thermodynamic Properties of Monocarboxylic Acids using Multiple Linear Regression

Topological indices are the numerical descriptor of a molecular structure obtained via molecular graph G. Topological indices are used for studying the properties of molecules such as structure-property relationship (QSPR), structure-activity relationship (QSAR) and structural design in chemistry, nanotechnology, and pharmacology. Its main role is to work as a numerical molecular descriptor in QSAR/QSPR models. Moreover, physicochemical properties such as boiling point, enthalpy of vaporization, and stability could be envisaged by QSAR/QSPR models. In this study, the QSPR (Quantitative Structure Property Relationship) model is designed using the Forgotten topological index, the Forgotten coindex, the max-min Rodeg index, and the inverse sum indeg index to predict the thermodynamic properties of monocarboxylic acids. The relationship analysis between the thermodynamic properties and the topological indices was done by using the multiple linear regression method.

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