发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究在单一嗅觉任务上微调的Uni-Mol2能否跨多种机器嗅觉任务迁移，发现其性能与专用基线相当且可稳定迁移，三维表征能区分镜像分子，支持机器嗅觉的跨任务迁移范式。
AI 中文摘要
基础模型已在分子性质预测领域实现变革，但目前仍不清楚，在单一标准嗅觉预测任务上微调的分子基础模型，是否能学习到可跨多种机器嗅觉问题迁移的表征。为探究该问题，本文在多标签气味描述符预测的GS-LF基准上微调Uni-Mol2，随后将所得模型无需额外深度学习训练，直接在四个互补下游场景评估：跨数据集气味描述符预测、有气味vs无气味分类、对映体评估及气味混合物区分度。微调后模型在主要GS-LF基准上的性能与嗅觉专用的最先进基线相当或更优，且在所有下游评估中均能稳定迁移。对映体分析进一步表明，三维分子表征可区分镜像分子，而二维图模型根本无法做到，不过准确预测立体化学的感知后果仍是悬而未决的挑战。综合来看，这些结果支持机器嗅觉的“一次训练、跨任务迁移”范式，表明经化学预训练的分子表征为可迁移的嗅觉预测提供了坚实基础。
英文摘要
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, enantiomer evaluation, and odor mixture discriminability. The fine-tuned model matches or exceeds the performance of the state-of-the-art olfaction-specific baseline on the primary GS-LF benchmark and consistently transfers across these downstream evaluations. The enantiomer analysis further shows that three-dimensional molecular representations distinguish mirror-image molecules in a way that two-dimensional graph models fundamentally cannot, although accurately predicting the perceptual consequences of stereochemistry remains an open challenge. Together, these results support a train-once, transfer-across-tasks paradigm for machine olfaction and suggest that chemically pretrained molecular representations provide a strong foundation for transferable olfactory prediction.
Comments18 pages, 6 figures. Supplementary information included
