A General-Purpose Molecular Foundation Model Transfers Across Diverse Olfactory Tasks

arXiv:2608.25893 · cs.LG · Submitted 2026-08-26 · Read on arXiv

cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/BioMachineLearning/openpom

Terminology

• Foundation Model — 28× in this paper · explained in 3 episodes
• Foundation Models — 13× in this paper · explained in 7 episodes
• AUROC — 11× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Transfer Learning — 7× in this paper · explained in 10 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Transferability — 5× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• EMMA — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• On the Opportunities and Risks of Foundation Models
• Semi-Supervised Classification with Graph Convolutional Networks
• Olfactory Label Prediction on Aroma-Chemical Pairs
• Foundation Models for Discovery and Exploration in Chemical Space
• Foundational Models Defining a New Era in Vision: A Survey and Outlook
• Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules

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