Learning Machines and Drug Discovery: A New Era in Cancer
Over the last several decades, technological advancements have revolutionized computer science to enable improvements in monitoring, comprehension, and decision-making in the field of cancer drug discovery. The widespread use of IT has led the shift away from traditional methods to working mostly with computers in many sectors and domains. Especially when dealing with large amounts of data, computational techniques can be very useful. There is an abundance of relevant data available to support the development and use of computation technologies such as AI, ML, DS, and BM. For drug discovery, data mining has become popular despite the complexity of specific fields, such as biology and medicine. A drug’s failure to succeed after release is further complicated by safety, regulatory, chemical, and biological factors. Recent post-market losses have led to serious safety concerns being raised, especially in cancer fields. It is no secret that failed medications have killed thousands of people and wrecked economies and countries throughout history. Thus, computational methods awaiting development may allow prediction of whether molecules in the pipeline will fail post-marketing. This Special Issue serves as a platform for researchers in this field of cancer drug discovery to share their knowledge and experience with others.
Dr. Satwinderjeet Kaur
Dr. Atiah H. Almalki
- drug discovery
- cancer bioinformatics
- deep learning
|Journal Name||Impact Factor||CiteScore||Launched Year||First Decision (median)||APC|
|4.2||4.0||2012||18.8 Days||CHF 2700||Submit|
|5.2||7.4||2009||18.2 Days||CHF 2900||Submit|
|4.6||6.7||1996||13.6 Days||CHF 2700||Submit|
International Journal of Molecular Sciencesijms
|5.6||7.8||2000||16.8 Days||CHF 2900||Submit|
|-||-||2021||16.6 Days||CHF 1000||Submit|
|2.6||2.6||1994||18.4 Days||CHF 2200||Submit|
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