DrugBank
PulseAugur coverage of DrugBank — every cluster mentioning DrugBank across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
-
LLMs like GPT-5.1 and Gemini 2.0 Pro show promise in biomedical knowledge graph reasoning
A new research paper benchmarks five large language models (LLMs) on their ability to process heterogeneous cognitive and biomedical knowledge graphs. The study found that general-purpose models like GPT-5.1 and Gemini …
-
New Biomedical Knowledge Graph Ecosystem Built on Rust Database
Researchers have developed a comprehensive biomedical knowledge graph ecosystem using the Samyama graph database, built with Rust. This system integrates multiple fragmented databases like Reactome, STRING, and DrugBank…
-
New DrugMCTS framework enhances drug repositioning with LLMs and multi-agent systems
Researchers have developed DrugMCTS, a new framework designed to improve drug repositioning using large language models. This system integrates retrieval-augmented generation (RAG), multi-agent collaboration, and Monte …
-
New framework DrugReason combines knowledge graphs and LLMs for drug repurposing
Researchers have developed DrugReason, a novel framework designed to enhance drug repurposing by integrating knowledge graph reasoning with large language model (LLM) insights. This approach addresses the limitations of…
-
New AI Framework Enhances Drug-Target Interaction Prediction
Researchers have developed ProbeMatchDTI, a novel framework designed to enhance drug-target interaction (DTI) prediction in AI-driven drug discovery. This method addresses limitations in existing approaches that can ove…
-
ProbeMatchDTI enhances AI drug discovery by capturing weak biochemical signals
Researchers have developed ProbeMatchDTI, a novel framework designed to improve drug-target interaction (DTI) prediction in AI-driven drug discovery. This method addresses limitations in existing approaches that tend to…
-
New AI Framework Predicts Therapeutic Response Using Gene Expression Data
Researchers have developed PREDIKTOR, a novel multi-view framework designed to predict patient-specific therapeutic response using gene expression data. This framework aligns a personalized gene regulatory network with …
-
New T2D-Bench framework evaluates LLM accuracy for Type 2 Diabetes
Researchers have developed T2D-Bench, a new evaluation framework designed to assess the accuracy and evidence-based reasoning of Large Language Models (LLMs) in the context of Type 2 Diabetes management. The framework u…
-
New MARD model advances drug-drug interaction prediction
Researchers have developed MARD (Mirror-Augmented Reasoning Distillation), a novel system for predicting mechanism-level drug-drug interactions (DDIs). MARD utilizes a 7B parameter model and incorporates three key train…