USE OF BIOINFORMATICS TO SELECT CANDIDATE DRUG MOLECULES
DOI:
https://doi.org/10.56238/levv16n53-039Keywords:
Cancer, New Drug Development, BioinformaticsAbstract
The development of new drugs can present several challenges, with a significant bottleneck being the ability to combine a molecule that is both a potent pharmacological inhibitor and achievable for synthesis. Prior computational studies must be performed to identify molecules with good pharmacokinetic parameters, high biological activity, and a high likelihood of being synthesized. Among the various computational tools used in the study of new drugs are those used to predict pharmacokinetic parameters. After identifying and validating a pharmacological target, such as kinase enzymes in cancer, promising molecules can be designed for this target. If the cancer is brain cancer, studies of promising molecules with the ability to cross the blood-brain barrier can be significantly beneficial. Therefore, the objective of this study is to conduct computational studies to select promising molecules for the treatment of brain cancer. Ninety-eight molecules were selected for this study. The selection criterion was, first, to assess the feasibility of synthesizing these molecules. Next, computational studies were performed to evaluate pharmacokinetic parameters, kinase inhibition, and blood-brain barrier crossing. The results of this study allowed the selection of five most promising molecules. These molecules can be synthesized and tested in vitro. Therefore, it can be concluded that this study allowed the reduction of a considered large number of proposed molecules to a small number of those considered most promising, thus reducing the time and cost required to develop promising molecules for the treatment of this type of cancer.
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