Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks

Author:

Kanev Georgi K.,Zhang Yaran,Kooistra Albert J.ORCID,Bender Andreas,Leurs Rob,Bailey David,Würdinger Thomas,de Graaf Chris,de Esch Iwan J. P.,Westerman Bart A.ORCID

Abstract

Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model’s root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.

Funder

Amsterdam Data Science

Brain Tumour Charity

Innovation Exchange Amsterdam (IXA) grant

Amsterdam University Medical Centers

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference89 articles.

1. Structural Chemogenomics: Profiling Protein–Ligand Interactions in Polypharmacological Space.;B Briels;Structural Biology in Drug Discovery: Methods, Techniques, and Practices.,2020

2. Overall survival in patients with pancreatic cancer receiving matched therapies following molecular profiling: a retrospective analysis of the Know Your Tumor registry trial;MJ Pishvaian;The Lancet Oncology,2020

3. The Drug Rediscovery protocol facilitates the expanded use of existing anticancer drugs;D Van der Velden;Nature,2019

4. A convergence-based framework for cancer drug resistance;DJ Konieczkowski;Cancer Cell,2018

5. The TICking clock of EGFR therapy resistance in glioblastoma: Target Independence or target Compensation;H Saleem;Drug Resist Updat,2019

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3