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 this https URL

利用 MolecularGPT，基于大规模语言模型对分子指令进行微调，并通过零样本和少样本的上下文学习，在少样本分子预测任务中取得新的竞争性结果，显示出语境推理能力优势，超过传统的图神经网络方法和现有语言模型基线，为语言模型在少样本分子特性预测中的应用发展提供了潜力。