Folding-Based End-To-End Chemical Drug Design with Uncertainty Estimation: Tackling Hallucination in the Post-GPT Era

In the post-GPT era, Llama-Gram represents a promising
advancement
in AI-driven chemical drug discovery, grounded in the chemical principle
that molecular structure determines properties. This folding-based
end-to-end framework seeks to address the hallucination issues of
traditional large language models by integrating protein folding embeddings,
graph-based molecular representations, and uncertainty estimation
to better capture the structural complexities of protein–ligand
interactions. By leveraging the frozen-gradient ESMFold model and
a Graph Transformer variant, Llama-Gram aims to enhance predictive
accuracy and reliability through grouped-query attention and a Gram
layer inspired by support points theory. By incorporating protein
folding information, the model demonstrates competitive performance
against state-of-the-art approaches such as Transformer CPI 2.0 and
Graph-DTA, offering improvements in compound–target interaction.
Llama-Gram provides a scalable and innovative chemical theory that
could contribute to accelerating the chemical drug discovery process.