While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.

中文翻译：

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piscesCSM：抗癌协同药物组合的预测


虽然药物组合疗法非常重要，特别是在癌症治疗中，但识别新型协同药物组合一直是一项具有挑战性的事业。在这种情况下，计算方法已成为一种有前途的工具，用于确定药物组合的优先顺序以进行进一步评估，尽管它们的性能、实用性和可解释性有限。在这里，我们提出了一种新型预测工具 piscesCSM，它利用基于图形的表示来模拟小分子化学结构，以准确预测对一种或多种癌细胞系具有良好抗癌协同作用的药物组合。利用这些见解，我们开发了一个通用监督机器学习模型来指导预测 30 多种细胞系中的抗癌协同药物组合。它在独立非冗余盲测中实现了高达 0.89 的受试者工作特征曲线下面积 (AUROC)，在大规模肿瘤筛查数据和阿斯利康生成的独立测试集上均优于最先进的方法（预测准确性提高了 16% 以上）。此外，通过探索我们方法的可解释性，我们发现简单的理化特性和基于图形的特征​​可以预测化疗的协同作用。为了提供一个简单且集成的平台来快速筛选具有良好协同抗癌作用的潜在候选对，我们在 https://biosig.lab.uq.edu.au/piscescsm/ 上免费在线提供 piscesCSM 作为网络服务器和 API。我们相信，我们的预测工具将为优化和增强组合筛选库提供宝贵的资源，以识别有效且安全的协同抗癌药物组合。 这项工作提出了 piscesCSM，这是一种基于机器学习的框架，它依赖于完善的基于图形的小分子表示来识别并提供同基因药物组合的更好的预测准确性。我们的模型 piscesCSM 表明，将物理化学特性与基于图的签名相结合可以在分类预测任务上优于当前的架构。此外，将我们的工具实现为网络服务器为研究人员提供了一个用户友好的平台，以筛选对一种或多种癌细胞系具有良好抗癌作用的潜在协同药物组合。