Various types of cancer may have different reactions to the same drug. AI can predict how cancerous cells will behave when treated with different compounds. This knowledge helps develop
new anticancer drugs and understand when to apply them. For example, a team of researchers developed a random forest algorithm, which can forecast anticancer drugs’ activity based on the cancer cell’s mutation state.
In another example, researchers at Aalto University, the University of Helsinki, and the University of Turku in Finland built an ML-powered model
, comboFM, that can determine which combination of drugs has the most potential to kill particular cancer cells. ComboFM uses historical data from experiments performed on similar cells and drugs when looking for the optimal combination. The researchers claim their software can serve as an effective way for pre-screening drug combinations for different oncology applications.
The influence of AI in cancer treatment expands to generic drugs as well. Laura Kleiman, the founder of Reboot Rx, worked with her team to build an AI-powered solution that would determine
which generic drugs have the potential for treating cancer. Currently, Laura focuses on prostate cancer. She employed Reboot Rx AI to analyze literature describing clinical studies of non-cancer drugs ever tested to treat prostate cancer. The algorithm itself can determine the relevance and significance of a study.