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3D Binding Mode Generative Pre-trained Transformer
(3bmGPT)

3bmGPT (3D Binding Mode Generative Pre-trained Transformer) is an AI model trained on text-converted binding interaction data.
Its purpose is to comprehend 3D interactions and transform them into analyzable features to find deeper insights into the complex nature of binding interactions.

3bmGPT (3D Binding Mode Generative Pre-trained Transformer) is an AI model trained on text-converted binding interaction data.
Its purpose is to comprehend 3D interactions and transform them into analyzable features to find deeper insights into the complex nature of binding interactions.
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Solution

Researchers encountered challenges in identifying key binding structures to design molecules that bind effectively to their interested targets.

Through 3bmGPT, diverse binding interactions for the molecules of interest whether protein or small molecule, are identified and summarized, enabling the discovery of key binding structures.

Scholars immerse themselves in the study of molecules, continually seeking additional binding details to further their academic pursuits.

AI-trained data-based studies will provide objective insights into binding interactions, broadening understanding and fostering innovative ideas.

Workflow

Training AI Model from Binding Interaction
Training AI Model from Binding Interaction
Utilizing the AI Model in Research
Utilizing the AI Model in Research
  • The AI model is trained using binding interaction data to predict 3D binding modes, categorized into all bindings. The GPT model analyzes these categories, along with protein pockets and ligands, to identify protein families and drug characteristics.
  • 3bmGPT can be used with complete binding data, protein-only data, or ligand-only data for diverse research purposes. These include tracking molecules, analyzing similar bindings, conducting phylogenetic research, and identifying key bindings linked to related drugs.

Application

  • 3bmGPT is invaluable for studying binding dynamics and advancing drug development efforts. To enhance its efficacy, Syntekabio offers the Auto-BP (Binding Pose) service, providing candidate ligands. These ligands can then be seamlessly integrated into 3bmGPT, allowing for a comprehensive analysis of the candidate molecules’ characteristics.
References:
  • OpenAI. “GPT-4 Technical Report.” ArXiv 2303.08774 (2023)
  • Ashwin Dhakal, et. al. “Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.” Briefings in Bioinformatics Vol23 (2022)

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