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Research questionWhen do self-supervised molecular graph representations improve property prediction beyond Morgan fingerprints?Pretraining can produce useful molecular embeddings without reliably improving downstream fine-tuning. Their value may depend on the data partition and whether embeddings are used alone or combined with fingerprints.
AI
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Machine Learning
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Statistical Machine Learning
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Latest papersRecent research connected to this question, newest first.Self-Supervised Pretraining of Molecular Graph Encoders with LeJEPAThe evidence concerns LeJEPA-pretrained GPS and Chemprop-style D-MPNN molecular graph encoders evaluated on an antibiotic-activity dataset and ogbg-molhiv. It covers frozen probes, fine-tuning, random and scaffold partitions, embedding dimensionality, and feature-level combination with Morgan fingerprints using multi-seed, bootstrap-based analyses; the results do not establish broader generalization beyond these datasets and settings.research paper · Sep 2, 2026
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