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LaMGen, a novel framework using large language models (LLMs), designs multi-target drugs with quantum accuracy. It generates novel drug candidates efficiently, outperforming existing methods for complex diseases.

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Artificial Intelligence

Background:

  • Current multi-target drug design methods lack biological context and generalizability.
  • Ligand-based approaches are limited to specific target pairs, hindering complex disease treatment.

Purpose of the Study:

  • Introduce LaMGen, a general-purpose framework for multi-target drug design.
  • Leverage large language models (LLMs) for accurate and efficient drug candidate generation.

Main Methods:

  • Utilized MTD2025 dataset with quantum-accurate molecular conformations and multi-target associations.
  • Integrated ESM-C protein embeddings, rotation-aware ligand tokens, and TriCoupleAttention module.
  • Developed a large language model (LLM)-powered framework for direct generation of energy-favorable conformations.

Main Results:

  • LaMGen demonstrated superior performance over diffusion-based models in independent benchmarks.
  • Generated molecules with high conformational plausibility in an average of 0.44 seconds.
  • Successfully reproduced known active molecules and generated novel candidates with enhanced binding affinities.

Conclusions:

  • LaMGen offers a powerful and generalizable approach for multi-target drug design.
  • The framework achieves quantum-level accuracy in predicting molecular conformations.
  • LaMGen accelerates drug discovery by efficiently generating novel, high-affinity drug candidates.