Name
Abstract | ||
|---|---|---|
With the continuous advancements of biomedical instruments and the associated ability to collect diverse types of valuable biological data, numerous recent research studies have been focusing on how to best extract useful information from the Big Biomedical Data currently available. While drug design has been one of the most essential areas of biomedical research, the drug design process for the most part has not fully benefited from the recent explosive growth of biological data and bioinformatics tools. With the significant overhead associated with the traditional drug design process in terms of time and cost, new alternative methods, possibly based on computational approaches, are very much needed to propose innovative ways to propose effective drugs and new treatment options. Employing advanced computational tools for drug design and precision treatments has been the focus of many research studies in recent years. For example, drug repurposing has gained significant attention from biomedical researchers and pharmaceutical companies as an exciting new alternative for drug discovery that benefits from the computational approaches. Molecular profiling of diseases can be used to design customised treatments and more effective approaches can be explored based on the individuals’ genotype. With the newly developed Bioinformatics tools, researchers and clinicians can repurpose existing drugs and propose innovative therapies and precision treatment options. This new development also promises to transform healthcare to focus more on individualized treatments, precision medicine and lower risks of harmful side effects. In particular, this potential new era in healthcare presents transformative opportunities to advance treatments for chronic and rare diseases. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-45385-5_20 | IWBBIO |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suyeon Kim | 1 | 0 | 0.34 |
| Ishwor Thapa | 2 | 10 | 6.69 |
| Farial Samadi | 3 | 0 | 0.34 |
| Hesham Ali | 4 | 9 | 4.86 |