TargetSage: Identifying Therapeutic Target Genes with Interpretable and Robust LLM Reasoning

Therapeutic target identification---determining which genes or proteins to modulate for a desired clinical effect---is a foundational step in drug discovery. However, existing computational methods that rank candidates at genome scale face three persistent challenges: (i) static feature design, which discards mechanistic knowledge locked in biomedical literature; (ii) lack of interpretability, offering no auditable gene-level rationale for individual predictions; and (iii) the false-negative problem, where unlabeled but potentially druggable genes are treated as definitive negatives. We propose TargetSage, an LLM reasoning agent for therapeutic target identification built on positive-unlabeled (PU) learning, with three modules. First, Agentic Profiling enriches each gene with LLM-gathered biomedical evidence, distilling 2,261 attributes into a 23-dimensional reasoning vocabulary. Second, Guided Reasoning produces gene-specific rationales and explicit reasoning attributes through a GRPO-trained policy rewarded by downstream Adjusted $F_1$. Third, PU-Aware Scoring integrates structured biological features, explicit reasoning attributes, and implicit reasoning embeddings via gated fusion under an LLM-informed class-prior to produce genome-wide druggability scores. Evaluated on 15 benchmark tasks spanning 19,032 human protein-coding genes, TargetSage consistently outperforms ten baselines, surpassing the strongest by 37% in macro-average Adjusted $F_1$ and generalizing to independent held-out benchmarks. Using only 2021 training data, TargetSage outperforms all ten baselines at ranking genes that gain clinical approval by 2025. On an independent validation set of Phase 2/3 clinical candidates, it further achieves a 51% higher enrichment odds ratio at the top-25 shortlist than the runner-up baseline. TargetSage provides an evidence-grounded, continuously updatable foundation for interpretable target prioritization at genome scale.