Artificial Intelligence and Cancer Drug Development

Abstract

Background: The development of cancer drugs is among the most focused “bench to bedside activities” to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and AI. In the meantime, challenges, like curating the data of low quality, remain to be resolved. Objective: This review focused on the recent advancements in and challenges of AI in developing cancer drugs. Method: We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies. Results and Conclusion: AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.

Future implications of ChatGPT in pharmaceutical industry: drug discovery and development

“…The COVID-19 pandemic has exposed various limitations in drug development, highlighting the need to accelerate the process ( Asselah et al, 2021 ). Traditional methods for identifying disease targets and potential therapeutic chemicals are often inefficient, requiring manual selection and validation or omics analysis by a bioinformatician ( Yang et al, 2022 ). Additionally, the vast amount of literature available makes it difficult to consult and extract meaningful information.…”

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

“…This expectation was shared by most of the respondents in this study. By analyzing data from clinical trials and other sources, AI algorithms can identify patterns that may indicate the potential effectiveness of a new drug [ 14 , 15 ]. This could help speed up the drug development process, potentially leading to new treatments that are more effective and have fewer side effects.…”

“…As is the case in many other fields of healthcare, the integration of AI in cancer care is expected to reshape the existing scenario in the future [ 10 ]. For example, as a predictive modeling and early detection, AI could be used to analyze data from a variety of sources, such as electronic health records, genetic information, and environmental data, to predict an individual’s risk of developing cancer and to tailor prevention strategies accordingly [ 13 , 14 , 15 , 16 ]. AI-related applications may reduce screening costs [ 17 ], provide more reliable diagnostics [ 13 , 18 , 19 , 20 ], improve prognostics [ 13 , 19 , 21 , 22 , 23 , 24 , 25 ], and aid in the discovery of new drugs [ 14 , 15 ].…”

“…For example, as a predictive modeling and early detection, AI could be used to analyze data from a variety of sources, such as electronic health records, genetic information, and environmental data, to predict an individual’s risk of developing cancer and to tailor prevention strategies accordingly [ 13 , 14 , 15 , 16 ]. AI-related applications may reduce screening costs [ 17 ], provide more reliable diagnostics [ 13 , 18 , 19 , 20 ], improve prognostics [ 13 , 19 , 21 , 22 , 23 , 24 , 25 ], and aid in the discovery of new drugs [ 14 , 15 ]. Several areas of cancer care are expected to benefit from AI-related applications, including cancer radiology and clinical oncology [ 10 ].…”

A review on mushrooms as a versatile therapeutic agent with emphasis on its bioactive constituents for anticancer and antioxidant potential

“…Bioinformatic techniques may be sound and effective in the process of identifying potential and safe anticancer drugs. Although this will help standardize treatment, complications may arise from interactions among different bioactive components [111,112].…”