Mew233/ddigpt

• highabout#1

  Add a concise 'About' description for the repository

  Why:

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  An explainable AI system for predicting Drug-Drug Interactions (DDIs) using Large Language Models (LLMs) enhanced with Knowledge Graphs, featuring a web server for interactive exploration and visualization.

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• hightopics#2

  Add relevant topics to the repository

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  drug-drug-interaction, ddi-prediction, large-language-models, llm, knowledge-graphs, explainable-ai, xai, bioinformatics, drug-discovery, cheminformatics, streamlit

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• mediumreadme#3

  Refine the README's opening paragraph for clarity and directness

  Why:

  CURRENT

  DDI-GPT web server for knowledge-enhanced DDI exploration. 1. The user submits a pair of drugs, referenced by DrugBank ID, the input sentence is generated by incorporating drug-related biomedical entities from KG. The input is then set to a cluster running the prediction pipeline, where it is processed. 2. Once the interface prediction is complete, the DDI-GPT web server will be updated to visualize importance words generated by explanation module. 3. DDI-GPT web server allows users to explore the KGs that were used by DDI-GPT framework. as well as various drug-protein-drug interaction networks and drug-side-effects-drug interaction network. 4. The user can view shortest paths or two-hop paths between the combination of drugs.

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  DDI-GPT is an explainable AI web server designed for predicting Drug-Drug Interactions (DDIs) by leveraging Large Language Models (LLMs) enhanced with Knowledge Graphs. It allows users to submit drug pairs, visualize predicted interactions with importance words from its explanation module, and explore the underlying biomedical knowledge graphs and interaction networks.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to predict potential drug interactions using AI and explainable insights?

  you: not recommended
  AI recommended (in order):

  1. DeepChem
  2. PyTorch Geometric
  3. Deep Graph Library
  4. RDKit
  5. scikit-learn
  6. XGBoost
  7. LightGBM
  8. TensorFlow
  9. Keras
  10. Chemprop
  11. Open Targets Platform

  AI recommended 11 alternatives but never named Mew233/ddigpt. This is the gap to close.

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• CATEGORY QUERY

  Tool for visualizing drug interaction networks and exploring biomedical knowledge graphs?

  you: not recommended
  AI recommended (in order):

  1. Cytoscape (cytoscape/cytoscape)
  2. Neo4j Bloom
  3. Neo4j Browser (neo4j/neo4j-browser)
  4. Graphistry
  5. Gephi (gephi/gephi)
  6. DataWalk
  7. yWorks yFiles

  AI recommended 7 alternatives but never named Mew233/ddigpt. This is the gap to close.

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• Metadata completeness

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  Suggestion:

• README presence

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• Compared to common alternatives in this category, what is the core differentiator of Mew233/ddigpt?

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  ✓AI named Mew233/ddigpt explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts Mew233/ddigpt in production, what risks or prerequisites should they evaluate first?

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  ✓AI named Mew233/ddigpt explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo Mew233/ddigpt solve, and who is the primary audience?

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  ⚠AI did not name Mew233/ddigpt — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?