Mitochem_AI

Model Card for Model ID

MitoChem-AI is a fine-tuned version of Qwen2.5-7B-Instruct (a 7B-parameter Large Language Model) specialized for Mitochondrial Biology and Chemical-Biological Inference. It is designed to perform advanced mechanistic reasoning—such as predicting novel protein-protein interactions ($\text{Drp}1$ regulation, $\text{SIRT}3$ signaling) and connecting complex, multi-organelle pathways (e.g., $\text{ER}$-Mitochondria crosstalk)—and to generate structured data outputs (e.g., $\text{JSON}$ entities) from domain-specific biological queries.

Model Details

Model Description

📝 Extended Model Summary

MitoChem-AI is a specialized Large Language Model (LLM) built upon the robust Qwen2.5-7B-Instruct foundation, fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with LoRA. It is custom-engineered to solve complex inference problems at the intersection of Mitochondrial Biology, Cellular Metabolism, and Chemical-Biological Informatics.

Unique Value Proposition

Unlike general-purpose $\text{LLMs}$ (which suffer from knowledge dilution and factual inaccuracies in highly specific niches), MitoChem-AI excels due to its constrained, high-quality fine-tuning dataset.

1. Native Cross-Domain Reasoning: The model is trained to natively link chemical structure features RDKit concepts, directly to specific mitochondrial functional outcomes.
2. Mechanistic Prediction: It specializes in generating plausible, testable hypotheses for sophisticated organelle crosstalk
3. Accuracy and Specificity: The model prioritizes technical accuracy and the use of precise scientific terminology, making it an ideal tool for research and drug discovery.

Core Domain Specialization

• Mitochondrial Dynamics:
• Metabolic Signaling:

• Developed by: [Kelechi Nwosu]
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Training Details

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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Technical Specifications [optional]

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Framework versions

• PEFT 0.18.0