用分子指纹来解决药物相互作用预测中的模型过度复杂性
Manel Gil-Sorribes1, Alexis Molina2
1Nostrum Biodiscovery, Barcelona, 08029, Spain.
Journal of cheminformatics
|February 19, 2026
概括
预测药物相互作用至关重要. 像摩根指纹 (MFP) 这样的简单分子表示在许多基准上与复杂模型相匹配或超过,这表明更好的数据集和评估是药物安全研究的关键.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 机器学习在药物发现中的作用
背景情况:
- 准确的药物相互作用 (DDI) 预测对于制药研究和患者安全至关重要.
- 目前的趋势有利于复杂的模型,但在标准基准上产生日益减少的回报.
- 评估分子表示是理解模型性能的关键.
研究的目的:
- 为了分离分子表示对DDI预测准确性的影响.
- 为了比较摩根指纹 (MFP),图形卷积网络 (GCN) 和MoLFormer嵌入的性能.
- 调查数据集分割对模型评估的影响.
主要方法:
- 使用了固定的分类器架构,只交换了分子表示.
- 比较ECFP4摩根指纹 (MFP),预训练的GCN和MoLFormer嵌入.
- 评估了DrugBank DDI分割和FDA药物-药物亲和力基准的模型,包括防泄漏和分销之外的分割.
主要成果:
- 浅头的MFP与标准的DrugBank和FDA基准标准上的更复杂模型相匹配或超越,使用更少的参数.
- 在未见的DDI分割上,MFP实现了AUROC 99.4和AUPR 98.4,超过了MoLFormer和之前的最先进状态.
- 一个预训练过的GCN在严格的支架上进行了分销之外的分割 (AUROC 73.99),强调了在具有挑战性的场景中模型容量的好处.
结论:
- 像MFP这样的简单分子表示对于DDI预测具有高度竞争力.
- 增加模型复杂性的绝对收益在标准基准上是最小的.
- 未来DDI预测的进展取决于改进的数据集策划和严格的分布外评估,而不仅仅是更复杂的模型.
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