Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction
arXiv:2603.12344v2 Announce Type: replace Abstract: Molecular Property Prediction (MPP) is a fundamental problem in drug discovery that has recently attracted growing attention. Large Language Models (LLMs), known for their impressive proficiency across domains, show promise as generalist models for MPP. However, their current performance remains below the threshold needed for practical adoption. To bridge this gap, we propose TreeKD for distilling the knowledge of tree-based specialist models into LLMs to complement the internal knowledge of LLMs and improve their predictive accuracy. For eac
The increasing limitations of large language models in specialized domains like molecular property prediction are driving research into novel knowledge distillation techniques to improve their practical applicability.
This development addresses a critical barrier to LLM adoption in scientific research and drug discovery, potentially accelerating innovation in these fields.
LLMs can now be enhanced with specialized knowledge from tree-based models, improving their accuracy and making them more viable tools for complex scientific problems.
- · AI researchers
- · Pharmaceutical companies
- · Biotechnology firms
- · Drug discovery platforms
- · Traditional drug discovery methods (longer term)
- · LLMs without domain-specific knowledge integration
- · Companies slow to adopt advanced AI in R&D
Improved accuracy of LLMs in specific scientific domains through knowledge distillation.
Faster and more efficient drug discovery processes due to AI-augmented research and development.
Potential for entirely new therapeutic discoveries and a reduction in R&D costs within the pharmaceutical industry.
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Read at arXiv cs.LG