PO.ET07.02 · 实验与分子治疗
drGT：利用药物-细胞-基因异质网络进行注意力引导的药物反应基因评估
drGT: Attention-guided gene assessment of drug response utilizing a drug-cell-gene heterogeneous network
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：仅有准确的药物反应预测不足以产生转化影响；计算模型还必须生成生物学上合理的假设。我们提出drGT，一种涵盖药物、基因和细胞系的异质图神经网络（GNN）模型，通过注意力系数（AC）将预测与面向机制的可解释性相耦合。
结果：drGT编码一个药物-基因-细胞系图，并使用AC来描述药物-基因关联。我们在GDSC1、GDSC2、NCI60和CTRP数据集上评估了预测泛化性（随机、未见药物、未见细胞和零样本划分）和生物学可信度（文本挖掘的PubMed共提及以及与基于结构的DTI预测器的比较）。在此，我们评估了回归（IC50值）和二元分类（细胞系敏感性）两种设置，以反映典型的药物基因组学实验；我们报告回归的R²和分类任务的AUROC。在各基准测试中，drGT始终提供顶尖的回归性能，同时保持具有竞争力的分类准确性。在随机5折交叉验证中，每折随机屏蔽20%的样本用于测试，drGT获得高达0.690的R²（在同类方法中总体排名第1）和高达0.945的AUROC（总体排名第3）。在留一泛化测试中，训练期间排除一个细胞系的所有样本或一种药物的所有样本，drGT分别取得0.692（第1）和0.022（第1）的R²值以及0.706（第3）和0.844（第2）的AUROC。在零样本预测测试中，在GDSC1上训练并在GDSC2上评估，drGT取得0.334（第1）的R²和0.786（第1）的AUROC，二者均为所有模型中的最高分。在可解释性方面，AC衍生的药物-基因关联复现了已知生物学：在NCI60数据集中976种具有已知DTI的药物中，36.9%的预测关联与已确立的DTI相符，63.7%得到PubMed摘要或最先进（SOTA）的基于结构的DTI预测模型的支持。此外，对AC排序基因的过表征分析识别出与作用机制（MoA）一致的通路，如相关激酶抑制剂的KRAS信号通路，为模型预测提供了通路水平的解释。
结论：drGT通过AC衍生的药物-基因关联推进了预测泛化性和以机制为中心的可解释性，提供了SOTA的回归准确性和文献支持的生物学假设，展示了可解释的图学习如何能够弥合AI预测与生物学发现之间的鸿沟。代码：https://github.com/sciluna/drGT
查看英文原文 English abstract
Background: Accurate drug response prediction alone is insufficient for translational impact; computational models must also generate biologically plausible hypotheses. We present drGT, a heterogeneous graph neural network (GNN) model over drugs, genes, and cell lines that couples prediction with mechanism-oriented interpretability via attention coefficients (ACs).
Results: drGT encodes a drug-gene-cell line graph and uses ACs to describe drug-gene associations. We assess both predictive generalization (random, unseen-drug, unseen-cell, and zero-shot splits) and biological credibility (text-mined PubMed co-mentions and comparison to a structure-based DTI predictor) on GDSC1, GDSC2, NCI60, and CTRP datasets. Here, we evaluate both regression (IC50 values) and binary classification (cell-line sensitivity) settings to reflect typical pharmacogenomic experiments; we report R² for regression and AUROC for classification tasks. Across benchmarks, drGT consistently delivers top regression performance while maintaining competitive classification accuracy. Under random 5-fold cross-validation, where 20% of samples are randomly masked for testing in each fold, drGT attains an R² of up to 0.690 (1st overall against similar methods) and an AUROC of up to 0.945 (3rd overall). In leave-one-out generalization tests, where either all samples from one cell line or all samples of one drug are excluded during training, drGT achieves R² values of 0.692 (1st) and 0.022 (1st) and AUROCs of 0.706 (3rd) and 0.844 (2nd), respectively. In the zero-shot prediction test, trained on GDSC1 and evaluated on GDSC2, drGT achieves an R² of 0.334 (1st) and an AUROC of 0.786 (1st), both of which represent the highest scores among all models. For interpretability, AC-derived drug-gene links recover known biology: among 976 drugs from the NCI60 dataset with known DTIs, 36.9% of predicted links match established DTIs, and 63.7% are supported by either PubMed abstracts or a state-of-the-art (SOTA) structure-based DTI prediction model. Moreover, an over-representation analysis of AC-ranked genes identifies mechanism of action (MoA)-consistent pathways, such as KRAS signaling, for relevant kinase inhibitors, providing pathway-level explanations for the model predictions.
Conclusions: drGT advances predictive generalization and mechanism-centered interpretability with AC-derived drug-gene links, offering SOTA regression accuracy and literature-supported biological hypotheses, demonstrating how interpretable graph learning can bridge AI prediction and biological discovery. Code: https://github.com/sciluna/drGT
利益披露 Disclosure
Y. Inoue, None..
H. Lee, None..
T. Fu, None..
R. Kuang, None..
A. Luna, None.