MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
<|reference_start|>MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction: Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery. However, prior methods are limited by the requirement for a large number of labeled molecules and their restricted ability to generalize for unseen and new tasks, both of which are essential for real-world applications. To address these challenges, we present MolecularGPT for few-shot MPP. From a perspective on instruction tuning, we fine-tune large language models (LLMs) based on curated molecular instructions spanning over 1000 property prediction tasks. This enables building a versatile and specialized LLM that can be adapted to novel MPP tasks without any fine-tuning through zero- and few-shot in-context learning (ICL). MolecularGPT exhibits competitive in-context reasoning capabilities across 10 downstream evaluation datasets, setting new benchmarks for few-shot molecular prediction tasks. More importantly, with just two-shot examples, MolecularGPT can outperform standard supervised graph neural network methods on 4 out of 7 datasets. It also excels state-of-the-art LLM baselines by up to 16.6% increase on classification accuracy and decrease of 199.17 on regression metrics (e.g., RMSE) under zero-shot. This study demonstrates the potential of LLMs as effective few-shot molecular property predictors. The code is available at https://github.com/NYUSHCS/MolecularGPT.<|reference_end|>
@article{liu2024moleculargpt:,
    title={MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular
  Property Prediction},
    author={Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, Qiaoyu Tan},
    journal={arXiv preprint arXiv:2406.12950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12950},
    primaryClass={q-bio.QM cs.AI cs.CE cs.CL cs.LG}
}