AI can improve drug combination design and personalized medicine

Credit: Zac Goh

A new auto-commentary that was published recently in the journal SLAS Technology talks about how an emerging area of artificial intelligence, especially the analysis of small systems-of-interest specific datasets, can now be used to significantly improve drug development and personalized medicine.

This new auto-commentary builds on a previous study that was recently published by the authors in Science Translational Medicine about an artificial intelligence (AI) platform, known as Quadratic Phenotypic Optimization Platform (QPOP), that has the potential to substantially improve combination therapy in bortezomib-resistant multiple myeloma to identify the best drug combinations for patients who suffer from individual multiple myeloma.

There’s now significant proof that complex diseases, such as cancer, often need effective drug combinations to achieve any significant therapeutic impact. As the drugs in these drug combination therapies become more and more specific to molecular targets, it makes designing effective drug combinations and also choosing the right drug combination for the right patient more and more difficult.

Artificial intelligence is now leading to a positive impact on drug development and personalized medicine. Along with the ability to efficiently analyze the small datasets that have the ability to focus on the specific disease of interest, QPOP and other small dataset-based AI platforms can intelligently design optimal drug combinations that are effectiver and based on real experimental data and not on the mechanistic assumptions or predictive modeling.

Additionally, due to the efficiency of the platform, QPOP can also used for the important patient samples to help optimize and personalize combination therapy.

The SLAS Technology auto-commentary, titled “Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine,” can be found here at


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