MOTIVATION The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process. RESULTS KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases. AVAILABILITY AND IMPLEMENTATION KinomeX is available at: https://kinome.dddc.ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

---

KinomeX：用于预测小分子在全基因组范围内的多药理作用的Web应用程序。

动机通过实验和传统的计算机模拟药物设计方法，对小分子进行大规模的全基因组虚拟分析是一项艰巨的任务。深度学习算法的最新进展为推动这一过程带来了新的机遇。结果KinomeX是一个在线平台，可仅基于小分子的化学结构来预测小分子在整个kinome范围内的多药理作用。预测是通过多任务深度神经网络模型进行训练的，该模型使用针对391种激酶的超过140 000个生物活性数据点进行了训练。已经进行了广泛的计算和实验验证。总体而言，KinomeX使用户能够创建一个全面的kinome相互作用网络，以设计新颖的化学调节剂，对于探索以前研究较少或未靶向的激酶具有实用价值。可用性和实现KinomeX的网址为：https://kinome.dddc.ac.cn。补充信息补充数据可从Bioinformatics在线获得。