Overcoming Topology Bias and Cold-Start Limitations in Drug Repurposing: A Clinical-Outcome-Aligned LLM Framework 1. This new study introduces a novel framework that addresses key limitations in drug repurposing, specifically targeting the issues of topology bias and cold-start scenarios in computational models. The framework leverages a clinical-outcome-aligned approach using large language models (LLMs) to enhance drug repurposing accuracy and robustness. 2. Traditional graph neural networks (GNNs) often struggle with inductive generalization and popularity bias, failing in cold-start scenarios where new compounds lack prior graph connectivity. The study demonstrates that their proposed DR-SFT model achieves remarkable performance in these challenging settings, with a top-10 precision of 0.80, significantly outperforming GNN baselines. 3. The innovation lies in the integration of clinical trial outcomes as rewards for model optimization. Using Kahneman-Tversky Optimization (KTO), the framework aligns model predictions with real-world clinical utility, effectively filtering out popular but ineffective drug candidates. This clinical alignment results in a top-10 precision of 0.90 in hard-negative tests, showcasing superior discriminative power. 4. Beyond repurposing accuracy, the model also achieves state-of-the-art performance on BioASQ and Chemprot benchmarks, demonstrating enhanced biomedical reasoning and factuality. The study further validates the model's predictions through molecular docking simulations and experimental confirmation, identifying BAY 61-3606 as a novel high-affinity FLT3 binder for acute myeloid leukemia (AML). 5. The research highlights the potential of combining semantic reasoning with clinical outcome alignment to bridge the gap between computational predictions and physical reality. This paradigm shift from structural probability to clinical utility offers a scalable and evidence-grounded path for identifying viable therapeutic candidates. 📜Paper: biorxiv.org/content/10.64898… 💻Code: github.com/cat-tontree/drkto #DrugRepurposing #LLM #ClinicalAlignment #ComputationalBiology #BiomedicalResearch

16/22 MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation This paper introduces MedCare, a Medical LLM that leverages a progressive fine-tuning pipeline to address knowledge-intensive and alignment-required tasks in medical NLP. The two-stage paradigm first encodes diverse knowledge while filtering detrimental information, and then focuses on alignment without knowledge interference. MedCare achieves state-of-the-art performance on over 20 medical tasks, including specific alignment tasks, outperforming existing models of similar sizes (1.8B, 7B, 14B). #MedCare #MedicalLLM #NLP #KnowledgeAggregation #ClinicalAlignment