It is now evident that complex diseases, such as cancer, often require effective drug combinations to make any significant therapeutic impact. As the drugs in these combination therapies become increasingly specific to molecular targets, designing effective drug combinations as well as choosing the right drug combination for the right patient becomes more difficult. Artificial intelligence is having a positive impact on drug development and personalized medicine. With the ability to efficiently analyze small datasets that focus on the specific disease of interest, QPOP, and other small dataset-based AI platforms can rationally design optimal drug combinations that are effective and based on real experimental data and not mechanistic assumptions or predictive modeling. Furthermore, because of the efficiency of the platform, QPOP can also be applied to previous patient samples to help optimize and personalize combination therapy. Reference: “Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine” by Masturah Bte Mohd Abdul Rashid and Edward Kai-Hua Chow, 24 September 2018, SLAS Technology.DOI: 10.1177/2472630318800774
