Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52% and a specificity of 55%. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein–small molecule interactions, when sufficient drug–target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.

目前 FDA 批准的激酶抑制剂会引起多种不良反应，其中一些是由于这些药物的机制独立效应。识别这些与机制无关的相互作用可以提高药物安全性并支持药物再利用。在这里，我们开发iDTPnd（带有负数据集的集成药物目标预测器），一种用于大规模发现已知药物新目标的计算方法。对于给定的药物，我们构建了一个正结构特征以及一个负结构特征，以捕获药物结合位点的弱保守结构特征。为了便于评估非预期目标，iDTPnd 还提供了基于对接的交互评分及其统计意义。我们以 52% 的敏感性和 55% 的特异性确认了索拉非尼、伊马替尼、达沙替尼、舒尼替尼和帕唑帕尼与其已知靶点的相互作用。我们还使用体外验证了 10 个预测的新靶点实验。我们的研究结果表明，激酶以外的蛋白质，如核受体、细胞色素 P450 和 MHC I 类分子，也可以是激酶抑制剂的生理相关靶标。当有足够的药物-靶点 3D 数据可用时，我们的方法普遍且广泛适用于识别蛋白质-小分子相互作用。构建结构签名的代码可在 https://sfb.kaust.edu.sa/Documents/iDTP.zip 获得。