An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives
1Department of Computer Science, Jamia Millia Islamia, New Delhi, India.
Abstract:
There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.
Insights
Artificial intelligence (AI) accelerates lung cancer drug development by analyzing data for personalized treatments. AI models achieve over 95% accuracy, revolutionizing anti-cancer therapeutics and saving researchers time.
Area of Science:
- Oncology
- Bioinformatics
- Computational Biology
Background:
- Lung cancer causes millions of deaths annually, necessitating rapid drug development.
- Over 100 types of human cancer highlight the urgent need for innovative therapeutic strategies.
- Artificial intelligence (AI) presents a pivotal solution for developing novel anti-cancer therapeutics.
Purpose of the Study:
- To review the diverse applications of AI in developing lung cancer therapeutics.
- To explore how AI optimizes therapeutic strategies and enhances treatment efficacy.
- To identify AI's role in personalized treatment recommendations and minimizing adverse effects.
Main Methods:
- A comprehensive literature review of articles indexed in PubMed and Scopus.
- Compilation and analysis of various machine learning approaches, including CNN, RNN, GAN, and VAEs.
- Validation of AI techniques through computer-aided drug design processes.
Main Results:
- AI models demonstrate high accuracy, exceeding 95%, in analyzing large datasets for drug design.
- Predictive models facilitate personalized treatment recommendations based on clinical and genetic data.
- AI streamlines drug development, enhancing efficiency and potentially reducing adverse effects.
Conclusions:
- AI holds transformative potential for lung cancer therapeutics, optimizing strategies and improving outcomes.
- AI application can significantly save time and effort for biological scientists in drug discovery.
- Further research is essential to overcome challenges and fully realize AI's capabilities in lung cancer treatment.
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