Fig. 3: Analysis of Drug MOAs and Gene Essentiality via GDSC Models.

A Workflow depicting the use of a large language model (LLM) for generating drug MOAs and identifying semantically relevant pathways. Starting from the drug’s available metadata, an LLM is repeatedly tasked with specialized prompts to generate a drug textual description. In parallel, PubMed is queried programmatically with the drug name to retrieve abstracts related to the drug. The information is integrated in a final textual description. The obtained drug description is used by a “Guided” LLM to choose which are the Reactome pathways which are most likely to modulate drug efficacy. This last procedure is repeated 5 different times and only pathways selected at least two times are retained; B Venn diagram showing the different drugs(top) and pathways (bottom) recovered using the LLM procedure as compared to pathway match based on drugs’ and target’s names; C Heatmap of significant MOA-pathways for various drug models, filtered by a correlation threshold ρ > 0.5. Drug names and involved pathways are labeled along the x-axis and y-axis, respectively. Starred squares highlight pathways linked to drugs via at least one annotation criterion. Adjacent bar plots show the count of significantly enriched elements per row/column in light gray, with those annotated by the pipeline in dark gray. A vertical dashed line highlights the presence of a group of drugs that most frequently recover pathways and known MOAs; D number of significantly enriched MOA-pathways obtained from different annotation criteria; E tissue-wise statistics of number of cell lines (top), number of core essential genes (middle), recall of essential genes at different important genes (SHAP) stringencies (top k 10, 20, 50, 100); F STRING PPI network of lung core essential genes recovered by SHAP importances. Nodes’ have diameters proportional to node degree and are colored according to SHAP values (the brighter the more important). Source data are provided as a Source Data file.