Molecular encoders: Pretrained 2D encoder (MoleculeSTM) and 3D encoder (Uni-Mol)
Blending Module: Combining complementary information from 2D and 3D encoders via cross-attention
Q-Former: Embed molecular representations into query tokens based on SciBERT
LoRA: Adapters for fine-tuning LLMs
Training Dataset
Mol-LLaMA is trained on Mol-LLaMA-Instruct, to learn the fundamental characteristics of molecules with the reasoning ability and explanbility.
Citation
If you find our model useful, please consider citing our work.
@misc{kim2025molllama,
title={Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model},
author={Dongki Kim and Wonbin Lee and Sung Ju Hwang},
year={2025},
eprint={2502.13449},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
