Intelligent Computational Approaches for Drug Design and Precision Medicine
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".
Deadline for manuscript submissions: closed (31 March 2021)
Special Issue Editor
Interests: bioinformatics; graph theory; design and analysis of algorithms; graph modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
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 data mining techniques. With the incredible overhead associated with the traditional drug design process in terms of time and cost, new alternative methods, based on computational approaches, are very much needed to propose innovative ways to propose effective drugs and new treatment options. The recent pandemic is a clear example of such a need where drug repurposing has been explored by biomedical researchers and pharmaceutical companies as a fast and effective alternative for traditional drug discovery that benefits from the availability of data and the recent advances of data mining approaches. This new development promises to transform healthcare to focus more on individualized treatments, precision medicine, and lower risks of harmful side effects. In this Special Issue, we invite the submission of papers that focus on exploring new data mining tools for advancing the new era of drug design and precision medicine.
Prof. Dr. Hesham H. Ali
Guest Editor
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Keywords
- Big biomedical data
- Drug design
- Data mining applications
- Drug repurposing
- Personalized medicine
- Data-driven healthcare
- Intelligent computational tools
- Data analytics
- Genetics
- Bioinformatics
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