KinomeX：一个用于预测小分子对激酶组广泛多效性影响的网络应用程序。

KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules.

机构信息

School of Information Management, Dezhou University, Dezhou, China.

Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.

出版信息

Bioinformatics. 2019 Dec 15;35(24):5354-5356. doi: 10.1093/bioinformatics/btz519.

DOI:10.1093/bioinformatics/btz519

PMID:31228181

Abstract

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.

摘要

动机

通过实验和传统的计算药物设计方法，对大规模的激酶组范围内的小分子进行虚拟分析是一项艰巨的任务。深度学习算法的最新进展为促进这一过程带来了新的机会。

结果

KinomeX 是一个在线平台，可仅基于小分子的化学结构预测其激酶组范围内的多效性。该预测是通过一个多任务深度神经网络模型完成的，该模型使用超过 140000 个生物活性数据点对 391 种激酶进行了训练。已经进行了广泛的计算和实验验证。总体而言，KinomeX 使研究人员能够为设计新型化学调节剂创建一个全面的激酶相互作用网络，并且在探索以前研究较少或未靶向的激酶方面具有实际价值。