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UCLA investigators have developed a new artificial intelligence-based platform that could help scientists identify promising drug combinations to overcome treatment resistance in cancer.

The platform, called MAESTRO and described in Advanced Science, uses large language models, or LLMs, to analyze scientific knowledge about how drugs work and the biological pathways cancer cells use to survive treatment. The system evaluates potential drug combinations, ranks the most promising candidates and generates biological explanations that researchers can test in the laboratory.

To identify drugs that could help overcome treatment resistance in prostate cancer, the researchers used MAESTRO, short for Mechanistic Augmentation and Ensemble Selection for Therapeutic Ranking Optimization, to evaluate thousands of natural products.

The platform identified several compounds that could potentially enhance the effectiveness of enzalutamide, a drug originally discovered and developed at UCLA that is commonly used to treat advanced prostate cancer.

"Developing drug combinations currently relies on either brute-force screening or researchers hypothesizing potential candidates based on existing literature," said Neil Lin, an associate professor of mechanical and aerospace engineering and bioengineering at the UCLA Samueli School of Engineering and senior author of the study.

"It is time-consuming, and it can still be difficult to capture the complex interactions among multiple biological pathways. MAESTRO could substantially accelerate this process.

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