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- ---
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- license: cc-by-nc-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-nc-4.0
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+ datasets:
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+ - NingLab/MuMOInstruct
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+ language:
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+ - en
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+ base_model:
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+ - meta-llama/Llama-3.1-8B-Instruct
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+ pipeline_tag: text-generation
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+ tags:
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+ - chemistry
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+ - molecule optimization
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+ ---
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+ ### Model Sources
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** https://github.com/ninglab/GeLLMO
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+ - **Paper:** https://arxiv.org/abs/2502.13398
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+
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+ ## Usage
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+
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+ For instructions to run the model, please refer to our repository.
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+
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+ ## Bias, Risks, and Limitations
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+ While our models are designed for research and drug discovery applications,
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+ they come with ethical and safety considerations:
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+
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+ 1. **Potential for Misuse:** Although the model is not explicitly designed to generate toxic,
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+ controlled, or harmful compounds, adversarial prompts or unintended biases in the pretrained model
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+ may lead to the generation of undesirable molecules.
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+ 2. **Unintended Harmful Outputs:** The model does not inherently filter out molecules with high toxicity,
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+ abuse potential, or environmental hazards. Users must implement additional safeguards to prevent misuse.
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+ 3. **Absence of Built-in Safety Mechanisms:** The model does not incorporate explicit regulatory or
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+ safety filters (e.g., toxicity or compliance checks).
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+ It is the responsibility of users to validate generated molecules for safety and ethical considerations.
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+
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+ We urge users to adopt best practices, including toxicity prediction pipelines,
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+ ethical oversight, and responsible AI usage policies, to prevent harmful applications of this model.
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+
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+ ## Citation
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+
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+ If you use the trained model checkpoints, datasets or other resources, please use the following citation:
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+ ```
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+ @misc{dey2025gellmo,
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+ title={$\mathtt{GeLLM^3O}$: Generalizing Large Language Models for Multi-property Molecule Optimization},
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+ author={Vishal Dey and Xiao Hu and Xia Ning},
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+ year={2025},
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+ eprint={2502.13398},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG},
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+ url={https://arxiv.org/abs/2502.13398},
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+ }
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+ ```