On molecular modeling and QSPR analysis of lyme disease medicines via topological indices

Lyme disease is the most widespread vector-borne infection in the United States, as well as in Asia and Europe. It is caused by the spirochete Borrelia burgdorferi and is transmitted through Ixodes (deer) tick bites. To analyze the structures of Lyme medicines, degree-based topological indices are used. These indices are functions that take numerical data about the structure and provide information about the physical and chemical properties of the drugs. Six properties of the drugs, such as boiling point, enthalpy, flash point, molar refraction, log P, and molar volume, are studied using topological indices and a linear regression model. This model is a theoretical approach to examining the relationship between the experimental and estimated values (topological indices) of drugs without conducting any laboratory experiments. The correlation coefficient values indicate the accuracy of the results, while the p-values indicate their significance. All the correlation coefficients are positive, demonstrating the direct correlation between topological indices and experimental values of Lyme. Statistical analysis show that most values of correlation are greater than 0.7, and p-values less than 0.05 validate the computations. Molecular descriptors and statistical techniques are invaluable tools for pharmacists and chemists in their research.

Automated summary

Lyme disease, caused by Borrelia burgdorferi and transmitted by Ixodes ticks, is a widespread vector-borne infection in the United States, Asia, and Europe. Topological indices are used to analyze the structures of Lyme medicines and study their physical and chemical properties. By applying a linear regression model, the correlation between topological indices and drug properties is examined, providing a theoretical approach to drug analysis without conducting lab experiments. The findings show a direct correlation between topological indices and experimental values, with most correlation coefficients above 0.7 and p-values less than 0.05, validating the computations.