An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

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.

Keywords:

• Artificial intelligence
• lung cancer
• drug designing
• machine learning
• predictive modelling
• deep learning
• cancer biology

Ethical responsibilities

This manuscript is a core literature review that does not require ethical clearance.

Author contributions

Shaban Ahmad; Conceptualisation, Data collection/curation, Investigation, Writing – original draft, review & editing Resources. Khalid Raza; Supervision, reviewing and editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Consent for publication

Both authors consent to submit the manuscript for publication to the Journal of Drug Targeting.

Availability of data and material

The collected data were from public repositories and the keywords provided in the manuscript.

Additional information

Funding

The author(s) reported that there is no funding associated with the work featured in this article.