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Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future

Mai Hanh Nguyen^1,2,3, Ngoc Dung Tran³, Nguyen Quoc Khanh Le^2,4,5,6

¹International Ph.D. Program in Cell Therapy and Regenerative Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan.

Current Medicinal Chemistry

|September 15, 2023

Abstract:

Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate for advanced-stage GC patients remains low. Consequently, there is an urgent need to identify novel drug targets and develop effective therapies. However, traditional drug discovery approaches are associated with high costs, time-consuming processes, and a high failure rate, posing challenges in meeting this critical need. In recent years, there has been a rapid increase in the utilization of artificial intelligence (AI) algorithms and big data in drug discovery, particularly in cancer research. AI has the potential to improve the drug discovery process by analyzing vast and complex datasets from multiple sources, enabling the prediction of compound efficacy and toxicity, as well as the optimization of drug candidates. This review provides an overview of the latest AI algorithms and big data employed in drug discovery for GC. Additionally, we examine the various applications of AI in this field, with a specific focus on therapeutic discovery. Moreover, we discuss the challenges, limitations, and prospects of emerging AI methods, which hold significant promise for advancing GC research in the future.

Insights

Artificial intelligence (AI) and big data are revolutionizing gastric cancer (GC) drug discovery. These technologies accelerate the identification of new drug targets and therapies, overcoming limitations of traditional methods for this prevalent cancer.

Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Gastric cancer (GC) is a leading cause of cancer death globally, with poor survival rates for advanced stages.
Traditional drug discovery for GC is costly, slow, and has a high failure rate.
There is a critical need for innovative therapeutic strategies and drug targets for GC.

Purpose of the Study:

To review the application of artificial intelligence (AI) and big data in gastric cancer drug discovery.
To highlight AI's role in identifying novel therapeutic targets and optimizing drug candidates for GC.
To discuss the challenges and future prospects of AI in advancing GC research.

Main Methods:

Review of current literature on AI algorithms and big data analytics in cancer drug discovery.
Analysis of AI applications specifically focused on therapeutic discovery for gastric cancer.
Examination of challenges and limitations associated with AI implementation in GC research.

Main Results:

AI and big data offer powerful tools to analyze complex biological datasets for GC.
AI can predict compound efficacy and toxicity, accelerating the drug development pipeline.
The integration of AI promises to enhance the efficiency and success rate of discovering new GC treatments.

Conclusions:

AI and big data are transforming gastric cancer drug discovery, offering new hope for patients.
AI-driven approaches can overcome the limitations of traditional methods, leading to faster development of effective therapies.
Continued research and development in AI hold significant promise for advancing gastric cancer treatment strategies.

Read full analysis

Keywords:

Artificial intelligence big data biomedical informatics cheminformatics drug discovery gastric cancer machine learning precision medicine.

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