Mao Yixiang, Ghosh Souparno, Pal Ranadip
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
Department of Statistics, University of Nebraska-Lincoln, Lincoln, NB, 68588, USA.
J Cheminform. 2025 Aug 28;17(1):128. doi: 10.1186/s13321-025-01071-8.
Quantitative structure-activity relationship (QSAR) modeling has become a critical tool in drug design. Recently proposed Topological Regression (TR), a computationally efficient and highly interpretable QSAR model that maps distances in the chemical domain to distances in the activity domain, has shown predictive performance comparable to state-of-the-art deep learning-based models. However, TR's dependence on simple random sampling-based anchor selection and utilization of radial basis function for response reconstruction constrain its interpretability and predictive capacity. To address these limitations, we propose Adaptive Topological Regression (AdapToR) with adaptive anchor selection and optimization-based reconstruction. We evaluated AdapToR on the NCI60 GI50 dataset, which consists of over 50,000 drug responses across 60 human cancer cell lines, and compared its performance to Transformer CNN, Graph Transformer, TR, and other baseline models. The results demonstrate that AdapToR outperforms competing QSAR models for drug response prediction with significantly lower computational cost and greater interpretability as compared to deep learning-based models.
定量构效关系（QSAR）建模已成为药物设计中的关键工具。最近提出的拓扑回归（TR）是一种计算效率高且具有高度可解释性的QSAR模型，它将化学领域的距离映射到活性领域的距离，其预测性能已显示出与基于深度学习的先进模型相当。然而，TR对基于简单随机抽样的锚点选择的依赖以及用于响应重建的径向基函数的使用限制了其可解释性和预测能力。为了解决这些限制，我们提出了具有自适应锚点选择和基于优化的重建的自适应拓扑回归（AdapToR）。我们在NCI60 GI50数据集上评估了AdapToR，该数据集包含60种人类癌细胞系中超过50,000种药物反应，并将其性能与Transformer CNN、Graph Transformer、TR和其他基线模型进行了比较。结果表明，与基于深度学习的模型相比，AdapToR在药物反应预测方面优于竞争性QSAR模型，具有显著更低的计算成本和更高的可解释性。
