ChatDrug

[Aspirit]

Description:
ChatDrug is an AI-driven conversational drug design model that leverages ChatGPT-like capabilities to facilitate interactive drug optimization. By integrating retrieval-based methods and domain-specific feedback, ChatDrug enables researchers to iteratively refine drug candidates using natural language prompts. This approach enhances user engagement in drug discovery, making AI-driven molecular design more accessible to chemists, bioinformaticians, and pharmaceutical researchers.

Key Features:

Conversational Drug Editing: Supports iterative molecule modification through text-based prompts.
Retrieval-Augmented Generation: Utilizes retrieval-based AI techniques for molecular optimization.
Domain-Specific Feedback Loop: Enhances accuracy by incorporating scientific feedback in drug design.
Applications: AI-powered lead optimization, small-molecule drug discovery, interactive molecular synthesis, and computational chemistry.

[Aspirit]

Publication:
📄 ChatDrug: ChatGPT-Powered Conversational Drug Editing Using Retrieval and Domain Feedback
🔗 Read the Paper​

Code Source:
📂 GitHub Repository: ChatDrug on GitHub​

[Aspirit]

This forum topic is for computational chemists, AI researchers, and drug discovery scientists interested in ChatDrug. Join this discussion to:

Explore how ChatDrug enhances interactive drug design with conversational AI.
Discuss strategies for integrating retrieval-augmented drug optimization.
Share real-world applications in small-molecule drug discovery and medicinal chemistry.
Collaborate on improving ChatDrug for broader applications in pharmaceutical research.
🚀 Join us in exploring AI-powered conversational drug design with ChatDrug!

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