Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.

Combination therapies are key to overcoming resistance to androgen receptor (AR) signaling inhibitors in prostate cancer. Current paradigms for developing synergistic therapies, however, remain chronically inefficient and resource-intensive due to the prohibitive scale of the combinatorial screening space and a lack of computational frameworks for navigating signaling crosstalk. This work introduces a hybrid in silico and in vitro lead discovery platform that integrates knowledge-augmented large language models (LLMs) with an oxygen-supplemented 3D spheroid system. By comparing compound mechanism-of-action annotations with disease-relevant signaling crosstalk, the LLM framework nominates drug pairs with predictive performance and interpretable, pathway-based rationales. This computational pipeline is complemented by an engineered 3D spheroid model that utilizes oxygen supplementation to mitigate artifactual necrosis, a common confounder that masks synergistic signals in standard screening. Using this approach to screen 3,592 natural products, we identified berberine-enzalutamide as an in vitro combination hit that re-sensitized resistant prostate cancer cells to AR blockade. Molecular and transcriptomic profiling revealed changes in mTORC1 and AMPK signaling that were consistent with the pathway-based hypothesis generated by LLM. These results establish proof of concept that mechanism-informed language modeling coupled with 3D screening can prioritize and experimentally evaluate drug-combination hypotheses at an early stage of discovery.