A General-Purpose Molecular Foundation Model Transfers Across Diverse Olfactory Tasks
Foundation models have transformed molecular property prediction, yet it remains unclear whether a molecular foundation model, fine-tuned on a single canonical olfactory prediction task, can learn representations that transfer across diverse machine olfaction problems. We investigate this question by fine-tuning Uni-Mol2 on the GS-LF benchmark for multi-label odor descriptor prediction and evaluating the resulting model, without additional deep-learning training, on four complementary downstream settings: cross-dataset odor descriptor prediction, odorous-versus-odorless classification, enantio
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- PossiblePossibly related (embedding) · 55%Machine learning helps Monell researchers begin mapping complex scents - Bioengineer.org →
- PossiblePossibly related (embedding) · 54%Complex odors prove easier to map than expected with machine learning - Phys.org →
- PossiblePossibly related (embedding) · 47%Vibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set - Nature →
- FuzzyOverlapping authors or contributors · 62%janhq/jan →
“Shared author/contributor keys: han”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ultralytics/ultralytics →
“Shared author/contributor keys: han”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Yikun Han →
“A General-Purpose Molecular Foundation Model Transfers Across Diverse Olfactory Tasks”