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Can computers conceive the complexity of cancer to cure it? Using artificial intelligence technology in cancer
Rachael C Adams1, Behnam Rashidieh1
1QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia.
Abstract:
Drug discovery and the development of safe and effective therapeutics is an intricate procedure, further complicated in the context of cancer research by the inherent heterogeneity and complexity of the disease. To address the difficulties of identifying, validating, and pursuing a promising drug target, artificial intelligence (AI) technologies including machine learning (ML) have been adopted at all stages throughout the drug development pipeline. Various methods are widely employed to efficiently process and learn from experimental data sets, with agent-based models garnering thorough interest due to their ability to model individual cell populations with aberrant phenotypes. The predictive power of artificial intelligence modelling techniques founded in comprehensive datasets and automated decision-making generates an obvious avenue of interest for application in the drug discovery pipeline.
Insights
Artificial intelligence (AI) and machine learning (ML) accelerate cancer drug discovery by analyzing complex data. These technologies aid in identifying and validating drug targets, improving therapeutic development.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Cancer drug discovery is complex due to disease heterogeneity.
- Identifying and validating drug targets presents significant challenges.
- Traditional methods struggle with the intricate nature of cancer research.
Purpose of the Study:
- To explore the application of artificial intelligence (AI) and machine learning (ML) in cancer drug discovery.
- To highlight the role of AI/ML in overcoming challenges in therapeutic development.
- To discuss the potential of advanced computational models in identifying viable drug targets.
Main Methods:
- Utilizing various machine learning (ML) algorithms for data analysis.
- Employing agent-based models to simulate aberrant cell populations.
- Leveraging AI for automated decision-making in the drug development pipeline.
Main Results:
- AI and ML techniques efficiently process and learn from large experimental datasets.
- Agent-based models offer insights into individual cell behaviors.
- AI-driven predictive modeling enhances drug target identification and validation.
Conclusions:
- AI and ML are transformative tools in cancer drug discovery.
- These technologies streamline the identification and validation of drug targets.
- AI-powered approaches promise to accelerate the development of effective cancer therapeutics.
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