Learning Machines and Drug Discovery: A New Era in Cancer

Topic Information

Dear Colleagues,

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
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biology biology	3.6	5.7	2012	16.1 Days	CHF 2700	Submit
Cancers cancers	4.5	8.0	2009	16.3 Days	CHF 2900	Submit
Molecules molecules	4.2	7.4	1996	15.1 Days	CHF 2700	Submit
International Journal of Molecular Sciences ijms	4.9	8.1	2000	18.1 Days	CHF 2900	Submit
BioMedInformatics biomedinformatics	-	1.7	2021	21.3 Days	CHF 1000	Submit
Current Oncology curroncol	2.8	3.3	1994	17.6 Days	CHF 2200	Submit

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