Early drug discovery is frequently bottlenecked by target identification, a challenge that becomes particularly difficult in complex diseases driven by overlapping, redundant pathways rather than a single dominant driver. Lung squamous cell carcinoma (LUSC) is one such disease: mutationally complex, lacking a dominant actionable target, and marked by a long series of failed single-agent targeted trials. We hypothesized a research environment, capable of reasoning across interconnected datasets, would be well-suited to generate novel, mechanistically grounded therapeutic hypotheses for LUSC. Emet, an AI research environment utilizing a biomedical knowledge graph of more than 1.5 billion triples connected to over 100 specialised biological databases, was paired with an Agentic Research Director that pursues each hypothesis through a graph-of-thoughts search, invoking scoped link-prediction and retrieval tools and committing every round of findings to persistent memory. The top-ranked hypotheses were two-target combinations rather than a single target. The four highest-ranked combinations were advanced to dose-matrix testing in NCI-H520 and SK-MES-1 cells, and two produced combination effects exceeding either single agent; to our knowledge neither of these two pairings had previously been evaluated in LUSC. Dual inhibition of CDC7 (TAK-931) and PKMYT1 (RP-6306) produced statistically significant synergy in both lines that strengthened from day 5 to day 7 (HSA 19.24 to 24.19 in NCI-H520; 11.07 to 13.73 in SK-MES-1). Dual inhibition of USP13 (spautin-1) and PI3K (alpelisib) produced an additive, cell-line-dependent effect, and immunoblotting confirmed the predicted mechanism: time-dependent depletion of MCL-1, c-Myc and SOX-2 driven by the USP13 arm. Agentic reasoning over multi-domain biomedical evidence can therefore nominate testable, mechanism-bearing combination hypotheses that survive experimental scrutiny.
Soman, J., Kundu, A., Newington, J., Wong, S., Desai, N., Leung, S., Cudini, J., Suarez, F., Grandsard, P.
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