A GPT-2 model fine-tuned with disease MeSH + protein sequence inputs and GRPO rewards produces more unique, valid, drug-like, high-PLAPT-affinity ligands than DrugGPT or DrugGen on five diabetic-nephropathy targets.
(16) Jablonka, K
3 Pith papers cite this work, alongside 40 external citations. Polarity classification is still indexing.
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Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
Fine-tuned LLaMA 3 achieves regression performance on QM9 molecular properties and 28 materials properties from composition strings that rivals random forests but is 5-10x worse than specialized models using atomic coordinates.
citing papers explorer
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DrugGen 2: A disease-aware language model for enhancing drug discovery
A GPT-2 model fine-tuned with disease MeSH + protein sequence inputs and GRPO rewards produces more unique, valid, drug-like, high-PLAPT-affinity ligands than DrugGPT or DrugGen on five diabetic-nephropathy targets.
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Heterogeneous Scientific Foundation Model Collaboration
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
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Regression with Large Language Models for Materials and Molecular Property Prediction
Fine-tuned LLaMA 3 achieves regression performance on QM9 molecular properties and 28 materials properties from composition strings that rivals random forests but is 5-10x worse than specialized models using atomic coordinates.