Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development
Sorin-Ștefan Bobolea1, Miruna-Ioana Hinoveanu1, Andreea Dimitriu1
1Department of Physical and Colloidal Chemistry, Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania.
Abstract:
Cancer affects one in three to four people globally, with over 20 million new cases and 10 million deaths annually, projected to rise to 35 million cases by 2050. Developing effective cancer treatments is crucial, but the drug discovery process is a highly complex and expensive endeavor, with success rates sitting well below 10% for oncologic therapies. More recently, there has been a growing interest in Artificial intelligence (AI) due to its potential to significantly enhance the success rates by processing large data sets, identifying patterns, and making autonomous decisions. The primary aim of this literature review is to examine the potential that state-of-the-art AI tech-nologies have to enhance and complement well-established research methods used in cancer drug development, such as QSAR, interactions prediction, and ADMET prediction, among others. The basic technical aspects of computational technologies are clarified, and key terms commonly asso-ciated with AI are defined. Current applications and case studies from academia and industry are presented to highlight AI's potential to accelerate progress in cancer drug research. Challenges and disadvantages of AI are also acknowledged, and it is discussed that future research should focus on overcoming its limitations to maximize its impact in cancer treatment.
Insights
Artificial intelligence (AI) offers a promising avenue to accelerate cancer drug discovery, a complex and costly process. By analyzing vast datasets and identifying patterns, AI can enhance traditional research methods and improve success rates for new cancer treatments.
Area of Science:
- Oncology
- Computational Biology
- Artificial Intelligence
Background:
- Cancer impacts a significant portion of the global population, leading to millions of deaths annually.
- Current cancer drug development is a lengthy, expensive, and low-success-rate process.
- Artificial intelligence (AI) presents an opportunity to improve cancer drug discovery efficiency and outcomes.
Purpose of the Study:
- To review the potential of AI technologies in cancer drug development.
- To explore how AI can enhance established research methods like QSAR and ADMET prediction.
- To define key AI terms and clarify computational technology aspects.
Main Methods:
- Literature review of state-of-the-art AI technologies.
- Examination of AI applications in cancer drug research.
- Analysis of case studies from academia and industry.
Main Results:
- AI can process large datasets to identify patterns and aid decision-making in drug discovery.
- AI has the potential to accelerate progress in developing novel cancer therapies.
- Current AI applications demonstrate promise in enhancing established research methodologies.
Conclusions:
- AI technologies can significantly enhance and complement existing cancer drug development methods.
- Addressing AI limitations is crucial for maximizing its impact on cancer treatment.
- Future research should focus on overcoming AI challenges to optimize its role in oncology.
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