MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
CoRR(2024)
Abstract
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
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.
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