title: DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule
    Graphs
summary: A ChatGPT-like system for drug compounds could be a game-changer in
  pharmaceutical research, accelerating drug discovery, enhancing our
  understanding of structure-activity relationships, guiding lead optimization,
  aiding drug repurposing, reducing the failure rate, and streamlining clinical
  trials. In this work, we make an initial attempt towards enabling ChatGPT-like
  capabilities on drug molecule graphs, by developing a prototype system
  DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound
  molecule graph and ask various questions about this compound. DrugChat will
  answer these questions in a multi-turn, interactive manner. The DrugChat system
  consists of a graph neural network (GNN), a large language model (LLM), and an
  adaptor. The GNN takes a compound molecule graph as input and learns a
  representation for this graph. The adaptor transforms the graph representation
  produced by the GNN into another representation that is acceptable to the LLM.
  The LLM takes the compound representation transformed by the adaptor and users'
  questions about this compound as inputs and generates answers. All these
  components are trained end-to-end. To train DrugChat, we collected instruction
  tuning datasets which contain 10,834 drug compounds and 143,517 question-answer
  pairs. The code and data is available at
  https://github.com/UCSD-AI4H/drugchat
paper_authors._id: 64fe6cc46da6fe9a6cf44c06
paper_authors.name: Youwei Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-11T11:10:11.607Z
paper_authors._id: 64a475aa33ded98516c6a37b
paper_authors.avatarUrl: /avatars/3abf5f8c7f56cc8612ab590f1b75c1d5.svg
paper_authors.fullname: Youwei Liang
paper_authors.type: user
paper_authors.user: youweiliang
paper_authors._id: 64fe6cc46da6fe9a6cf44c07
paper_authors.name: Ruiyi Zhang
paper_authors._id: 64fe6cc46da6fe9a6cf44c08
paper_authors.name: Li Zhang
paper_authors._id: 64fe6cc46da6fe9a6cf44c09
paper_authors.name: Pengtao Xie
paper_title: DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule
    Graphs
paper_summary: A ChatGPT-like system for drug compounds could be a game-changer in
  pharmaceutical research, accelerating drug discovery, enhancing our
  understanding of structure-activity relationships, guiding lead optimization,
  aiding drug repurposing, reducing the failure rate, and streamlining clinical
  trials. In this work, we make an initial attempt towards enabling ChatGPT-like
  capabilities on drug molecule graphs, by developing a prototype system
  DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound
  molecule graph and ask various questions about this compound. DrugChat will
  answer these questions in a multi-turn, interactive manner. The DrugChat system
  consists of a graph neural network (GNN), a large language model (LLM), and an
  adaptor. The GNN takes a compound molecule graph as input and learns a
  representation for this graph. The adaptor transforms the graph representation
  produced by the GNN into another representation that is acceptable to the LLM.
  The LLM takes the compound representation transformed by the adaptor and users'
  questions about this compound as inputs and generates answers. All these
  components are trained end-to-end. To train DrugChat, we collected instruction
  tuning datasets which contain 10,834 drug compounds and 143,517 question-answer
  pairs. The code and data is available at
  https://github.com/UCSD-AI4H/drugchat
paper_ai_summary: A graph neural network and large language model integrated system, DrugChat, enables interactive queries about drug compounds,加速ing pharmaceutical research.
paper_ai_keywords: graph neural network (GNN) large language model (LLM) drug molecule graphs structure-activity relationships lead optimization drug repurposing clinical trials multi-turn interaction end-to-end training