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In-Context Learning for Few-Shot Molecular Property Prediction
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In-Context Learning for Few-Shot Molecular Property Prediction
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In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model parameters. However, it is restricted to applications in natural language and inapplicable to other domains. In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning. On the FS-Mol and BACE molecular property prediction benchmarks, we find this method surpasses the performance of recent meta-learning algorithms at small support sizes and is competitive with the best methods at large support sizes.
Forward citations
Cited by 2 Pith papers
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AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
AbICL adapts antibody affinity ranking at test time by conditioning on labeled pairwise comparisons through a transformer context head trained with episodic meta-learning, achieving improved AUROC on the AbRank benchmark.
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ReCoG: Relational and Compact Context Graph Learning for Few-shot Molecular Property Prediction
ReCoG is a context graph learning framework with relational learning and information bottleneck modules for few-shot molecular property prediction.
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