COMPUTATIONAL STUDIES FOR NEW POSSIBILITIES IN THE SYNTHESIS OF DRUG CANDIDATE MOLECULES
DOI:
https://doi.org/10.56238/levv17n57-024Keywords:
In Silico Studing, Bioinformatics, Glioblastoma, Drug DiscoveryAbstract
The discovery of new drugs is a challenging field involving multiple stages of testing and regulatory approval. However, it holds the potential to transform disease treatment, improve patients’ quality of life, and even save lives. Bioinformatics plays a fundamental role in drug development, enabling a more efficient and precise approach to the discovery and optimization of new therapeutic agents. Virtual screening, for instance, employs computational simulations to identify the most promising molecules for specific targets. This allows for a faster and more targeted selection of potential drug candidates, reducing the number of compounds that need to be experimentally tested. After the identification and validation of a pharmacological target—such as kinase enzymes in cancer—it becomes possible to design promising molecules for that target. In the case of brain cancers, such as glioblastoma, studies focused on the ability of molecules to cross the blood–brain barrier can contribute significantly. In this context, previous computational studies were carried out to select molecules with favorable pharmacokinetic parameters, high biological activity, and feasible synthetic potential. The objective of this work was to perform in silico studies to select promising molecules that could be proposed as new potential drug candidates for glioblastoma treatment. An in silico study involving 57 molecules was conducted. All proposed molecules were evaluated using computational platforms to assess pharmacokinetic parameters such as oral bioavailability according to Lipinski’s “Rule of Five,” kinase inhibition potential, and ability to cross the blood–brain barrier. In this study, it was possible to identify a group of the most promising molecules. Molecules 20, 39, 44, 45, 51, and 55 demonstrated potential to cross the blood–brain barrier, according to the final Score defined in this study. Molecules 20, 39, 45, 51, 54, 55, 56, and 57 showed promising kinase inhibition activity based on their calculated pIC₅₀ values, indicating them as the most promising candidates. Notably, molecule 39 stood out by presenting the highest estimated pIC₅₀ value and the highest overall Score in the screening. Therefore, through this in silico study, molecule 39 can be highlighted as the most promising candidate for synthesis and in vitro testing.
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