DrugRepoChatter
A Drug Repurposing Chatting Expert
The exponential growth of scientific literature presents a significant challenge in drug discovery research.Keeping pace with the latest developments and efficiently extracting relevant information has become increasingly difficult.To address this challenge, we have developed DrugRepoChatter, an AI-powered assistantdesigned to facilitate efficient and accurate information retrieval within a large corpus of scientific documents.
AN AI-POWERED ASSISTANT FOR ACADEMIC RESEARCH
DrugRepoChatter is an innovative tool aimed at assisting researchers in navigating and extracting relevant information from a vast collection of scientific literature. By leveraging advanced AI techniques, including large language models and retrieval augmented generation, it provides researchers with a streamlined method to search, access, and synthesize scientific literature relevant to their work. This tool significantly improves the efficiency and effectiveness of the research process in drug discovery and repurposing.
Ask away - your AI expert is here to help
Natural language querying of scientific literature and AI-powered responses based on the content of your knowledge base
Custom search and knowledge base
Create and manage personal knowledge bases and customize search parameters for fine-tuned results
A drug repurposing focus
Integrated with a curated database of drug repurposing literature
HOW TO START A CONVERSATION
First things first: create an account or log in to access all the features DrugRepoChatter has to offer!
STEP 1
CONFIGURE A KNOWLEDGE BASE
Select an existing knowledge base or create a new one.
The default knowledge base, created by the REPO4EU consortium, contains full-text of 285 carefully curated publications on drug repurposing. To create a new one, upload PDF files and give your index a unique name.
STEP 2
USING THE Q&A FEATURE
Navigate to the Q&A page and adjust the search parameters. Type your question in the chat input and view the AI-generated response and sources. Use the "Clear Chat" button to start a new conversation.
This database was created through a rigorous curation process, and collates articles sourced from PubMed and Google Scholar. Each article was evaluated for relevance, quality, and contribution to drug repurposing. Inclusion criteria ensured programmatic usability, community acceptance, and open access. The curation process focused on tools and methods with programmatic interfaces (API, code) or user-friendly interfaces, databases and data resources, publications with significant citations or recent high-impact works, exclusively open-access articles for unrestricted access.
To customize your search: score threshold (0.0-1.0) sets the minimum relevance score for retrieved documents, k (1-50) determines the number of most relevant documents to return, and fetch_k (1-50) sets the initial number of documents to retrieve before filtering.