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English(EN) MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning

新基础模型通过准确的ADMET预测加速药物发现

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了MEGA-CL，一种旨在预测小分子吸收、分布、代谢、排泄和毒性（ADMET）特性的新型基础模型。该图神经网络框架利用对比学习和外部注意力机制来有效建模分子结构和关系。MEGA-CL在多个基准数据集和下游ADMET任务中表现出卓越的性能，在外部验证中显示出强大的泛化能力，并实现了临床相关的预测准确性。 AI

影响 通过提供准确的预测，加速了计算机ADMET评估和早期候选药物优化。

排序理由 该集群描述了一篇详细介绍用于分子ADMET预测的新基础模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新基础模型通过准确的ADMET预测加速药物发现

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该集群描述了一篇详细介绍用于分子ADMET预测的新基础模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tinghui Jin, Kedu Jin, Ying Li, Guanghui Ren, Jingzhi Xue, Shiyu Zhou, Xiaoli Dai, Li-bin Wei, Xijing Chen, Di Zhao, Jinfeng Liu ·

    MEGA-CL：通过图外注意力与对比学习实现可泛化ADMET预测的分子基础模型

    arXiv:2607.24314v1 Announce Type: new Abstract: Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery. Here, we present MEGA-CL, a foundation graph neural network framework for…

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