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	7.4	2012	16.4 Days	CHF 2700
Cancers cancers	4.5	8.8	2009	17.4 Days	CHF 2900
Molecules molecules	4.2	8.6	1996	15.1 Days	CHF 2700
International Journal of Molecular Sciences ijms	4.9	9.0	2000	16.8 Days	CHF 2900
BioMedInformatics biomedinformatics	-	3.4	2021	22 Days	CHF 1000
Current Oncology curroncol	2.8	4.9	1994	19.8 Days	CHF 2200

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All Biology Cancers Molecules IJMS BioMedInformatics Current Oncology

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14 pages, 3269 KiB  

Open AccessReview

The Application of Machine Learning on Antibody Discovery and Optimization

by Jiayao Zheng, Yu Wang, Qianying Liang, Lun Cui and Liqun Wang

Molecules 2024, 29(24), 5923; https://doi.org/10.3390/molecules29245923 - 16 Dec 2024

Cited by 2 | Viewed by 3271

Abstract

Antibodies play critical roles in modern medicine, serving as diagnostics and therapeutics for various diseases due to their ability to specifically bind to target antigens. Traditional antibody discovery and optimization methods are time-consuming and resource-intensive, though they have successfully generated antibodies for diagnosing [...] Read more.

Antibodies play critical roles in modern medicine, serving as diagnostics and therapeutics for various diseases due to their ability to specifically bind to target antigens. Traditional antibody discovery and optimization methods are time-consuming and resource-intensive, though they have successfully generated antibodies for diagnosing and treating diseases. The advancements in protein data, computational hardware, and machine learning (ML) models have the opportunity to disrupt antibody discovery and optimization research. Machine learning models have demonstrated their abilities in antibody design. These machine learning models enable rapid in silico design of antibody candidates within a few days, achieving approximately a 60% reduction in time and a 50% reduction in cost compared to traditional methods. This review focuses on the latest machine learning-based antibody discovery and optimization developments. We briefly discuss the limitations of traditional methods and then explore the machine learning-based antibody discovery and optimization methodologies. We also focus on future research directions, including developing Antibody Design AI Agents and data foundries, alongside the ethical and regulatory considerations essential for successfully adopting machine learning-driven antibody designs. Full article

(This article belongs to the Topic Learning Machines and Drug Discovery: A New Era in Cancer)

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