Protein-Ligand Binding Words for 3D-Aware LLM Training
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Solution Overview
Problem
Existing artificial intelligence models struggle to accurately predict protein-ligand interactions due to the loss of complex three-dimensional spatial information when converting binding structures into a form processable by Large Language Models (LLMs).
Innovation Solution
Convert the binding structure between a ligand and a protein into a text form using binding words, generate training data, and train the LLM using this data, incorporating electron donor-acceptor interactions and protein residue information to preserve three-dimensional spatial details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If binding structure is converted to text form for LLM processing, then processability by Large Language Model is improved, but loss of three-dimensional spatial information increases
Solution Approach 1:
The patent applies dimensionality change by representing three-dimensional binding structures through text-based binding words that encode spatial relationships. The binding structure conversion process transforms 3D structural data into a textual format that preserves essential spatial information through carefully designed binding word representations, enabling LLM processing while mitigating information loss.
Solution Approach 2:
The patent introduces binding words as an intermediary representation between the original three-dimensional binding structure and the LLM processing system. These binding words serve as a mediator that encodes spatial and interaction information in a text format that LLMs can process effectively while retaining crucial structural details.
2Measurement precision
If detailed binding structure information is preserved, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex binding structure information into discrete binding words, each representing specific interaction features between ligand and protein. This segmentation allows detailed information to be preserved in a structured, modular format that is more manageable and processable, reducing overall data processing complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms complex structural data into parameterized binding words that capture essential interaction characteristics. By changing the representation parameters from raw structural coordinates to meaningful binding interaction descriptors, the system preserves accuracy while simplifying processing.
Data Source
AI summary
Disclosed is a method performed by a computing device. The method may include a method for training an artificial intelligence model by using interaction data between a protein and a ligand. The method may include: converting a binding structure between a ligand and a protein into at least one binding word in text form which is processable in an artificial intelligence-based Large Language Model (LLM); generating training data using the at least one binding word; and training the LLM using the training data.