English(EN) Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning

研究质疑分子表示学习的进展，发现ECFP优于大多数神经网络模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

一项发表在arXiv上的新研究评估了28种预训练分子嵌入模型，包括图神经网络、文本Transformer和混合架构，涵盖25个分类数据集。研究发现，除CheMeleon外，大多数神经网络模型并未优于经典的ECFP计数指纹。在外部数据集上的进一步验证也未显示出任一嵌入方法具有决定性优势。研究最后建议在QSAR/QSPR研究中评估ECFP指纹和CheMeleon嵌入，并对分子表示学习的感知进展提出质疑。 AI

影响挑战了分子表示学习的感知进展，并暗示需要更严格的评估标准。

排序理由该集群包含一篇详细介绍AI模型基准测试的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Mateusz Praski, Jakub Adamczyk, Wojciech Czech · 2026-11-05 05:00

预训练分子嵌入模型在分子表示学习中的基准测试

arXiv:2508.06199v5 Announce Type: replace-cross Abstract: Pretrained molecular embedding models spanning graph neural networks, text transformers, and hybrid architectures have been widely used in drug discovery, yet no large-scale, rigorous comparison of their representations ex…

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预训练分子嵌入模型在分子表示学习中的基准测试

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