Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction


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Abstract

:Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors is complex and regulated by many factors, and the treatment of a single drug is easy to cause the human body to produce a drug-resistant phenotype to specific drugs and eventually leads to treatment failure. In the process of clinical tumor treatment, the combination of multiple drugs can produce stronger anti-tumor effects by regulating multiple mechanisms and can reduce the problem of tumor drug resistance while reducing the toxic side effects of drugs. Therefore, it is still a great challenge to construct an efficient and accurate screening method that can systematically consider the synergistic anti- tumor effects of multiple drugs. However, anti-tumor drug synergy prediction is of importance in improving cancer treatment outcomes. However, identifying effective drug combinations remains a complex and challenging task. This review provides a comprehensive overview of cancer drug synergy therapy and the application of artificial intelligence (AI) techniques in cancer drug synergy prediction. In addition, we discuss the challenges and perspectives associated with deep learning approaches. In conclusion, the review of the AI techniques' application in cancer drug synergy prediction can further advance our understanding of cancer drug synergy and provide more effective treatment plans and reasonable drug use strategies for clinical guidance.

About the authors

Zheng Peng

Department of Clinical Laboratory, Liuzhou Traditional Chinese Medical Hospital

Email: info@benthamscience.net

Yanling Ding

Department of Clinical Laboratory, Liuzhou Maternity and Child Healthcare Hospital

Email: info@benthamscience.net

Pengfei Zhang

Department of Pulmonary and Critical Care Medicine, Liuzhou Traditional Chinese Medical Hospital

Email: info@benthamscience.net

Xiaolan Lv

Department of Clinical Laboratory, Liuzhou Maternity and Child Healthcare Hospital

Email: info@benthamscience.net

Zepeng Li

Department of Infectious Disease, Liuzhou Traditional Chinese Medical Hospital

Author for correspondence.
Email: info@benthamscience.net

Xiaoling Zhou

Department of Gastroenterology, Liuzhou Traditional Chinese Medical Hospital

Author for correspondence.
Email: info@benthamscience.net

Shigao Huang

Department of Radiation Oncology, Xijing Hospital,, Fourth Military Medical University

Author for correspondence.
Email: info@benthamscience.net

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