arXiv:2606. 31085v1 Announce Type: new Abstract: Drug-drug interaction (DDI) prediction is essential for medication safety, yet it requires reasoning over heterogeneous biomedical evidence whose relevance changes across interaction mechanisms.
arXiv:2608. 06713v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unregistered molecules disconnected.
arXiv:2603.08166v2 Announce Type: replace Abstract: Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological...
arXiv:2610.14474v1 Announce Type: cross Abstract: Medication recommendations seek to deliver personalized drug combinations based on patients' clinical information, where ensuring transparent decisio...
arXiv:2607. 04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles.
arXiv:2507.07426v4 Announce Type: replace Abstract: Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their...
arXiv:2608. 06253v1 Announce Type: new Abstract: Metabolomics knowledge is distributed across heterogeneous resources and remains difficult to translate into predictive representations.
arXiv:2607. 02928v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction for new drugs is critical for minimizing unexpected adverse drug reactions.
Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard to interpret and unstable, whereas knowledge-graph methods provide mechanistic context yet remain static and fail to capture drug-induced transcriptomic perturbation dynamics.
arXiv:2606. 15931v1 Announce Type: cross Abstract: Historical medical archives and traditional medicines hold immense potential for drug discovery and remain a primary source for current drug development.
MedResearcher‑R1 is a medical deep‑research agent that uses a Knowledge‑Informed Trajectory Synthesis (KISA) framework to build medical knowledge graphs and generate complex multi‑hop question‑answer pairs, especially for rare medical entities. It also incorporates a medical retrieval engine and general‑purpose tools to reliably synthesize medical evidence. The resulting 2,100+ trajectories across 12 specialties enable supervised fine‑tuning and reinforcement learning, producing a 32B model that outperforms larger proprietary systems on MedBrowseComp and shows strong performance on GAIA and xBench benchmarks.
arXiv:2606. 07698v1 Announce Type: cross Abstract: Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graphs.