An Extensive Review on Lung Cancer Therapeutics Using Machine Learning Techniques: State-of-the-art and Perspectives.

Lung cancer starts when lung cells grow uncontrollably, forming tumours that make breathing difficult. There are more than 100 types of human cancer, and in most cases, it is untreatable due to the unavailability of medico-infrastructure and facilities, even though the USFDA approved 57 anticancer drugs in 2020 alone. WHO reported more than 10 million cancer-related deaths yearly, and lung cancer alone accounts for more than 1.80 million deaths and a few studies suggest lung cancer incidence and deaths may surpass 3.8 million and 3.2 million by 2050, which demands rapid drug designing and repurposing and the role of artificial intelligence (AI) found to be the best solutions. AI in lung cancer therapeutics has emerged as a significant area of research in recent years. This state-of-the-art review aims to explore the various applications of AI in lung cancer treatment and its potential to revolutionise patient care, and predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, having the potential to optimise therapeutic strategies, enhance treatment efficacy, and minimise adverse effects. Methods: A thorough and extensive literature review was conducted after reading relevant research papers and book chapters of the last decade, indexed in PubMed and Scopus to get high-quality articles to compile this article. Several engineering conference proceedings have also been included, as they meet our quality review standards. Results: Advanced algorithms accelerate the process and improve efficiency, with accuracy beyond 95% in many cases, validated with traditional computational drug designing and repurposing approaches such as Molecular Docking and Dynamic Simulations. We have also compiled the use of convolutional neural networks, recurrent neural networks, generative adversarial networks, variational autoencoders, reinforcement learning, and many more. Conclusion: The role of AI in lung cancer therapeutics holds excellent promise through accurate detection, personalised treatment planning, novel drug design, drug repurposing, and decision support. AI can potentially transform lung cancer therapeutics by providing a robust solution that is most accurate in the least time, which can save the time and effort of experimental biological scientists. Advanced AI algorithms such as Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Variational Autoencoders, and Reinforcement Learning have been used in various drug repurposing articles, and even the drugs and vaccines are in clinical trial stages in just years which earlier were taking decades to get a drug or vaccine in market, and the SARS CoV-2 vaccine is the result for the same. However, further research and collaboration are required to address the existing challenges and fully realise the potential of AI in this field.