Journal ArticleDOI
Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation
Xutong Li,Zhaojun Li,Xiaolong Wu,Xiaolong Wu,Zhaoping Xiong,Zhaoping Xiong,Tianbiao Yang,Zunyun Fu,Xiaohong Liu,Xiaohong Liu,Xiaoqin Tan,Feisheng Zhong,Xiaozhe Wan,Dingyan Wang,Xiaoyu Ding,Ruirui Yang,Ruirui Yang,Hui Hou,Hui Hou,Chunpu Li,Hong Liu,Kaixian Chen,Kaixian Chen,Hualiang Jiang,Hualiang Jiang,Mingyue Zheng +25 more
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TLDR
A virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm is presented to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning.Abstract:
The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm. The obtained model yields excellent internal prediction capability with an auROC of 0.90 and consistently outperforms conventional single-task models on external tests, especially for kinases with insufficient activity data. Moreover, more rigorous experimental validations including 1410 kinase-compound pairs showed a high-quality average auROC of 0.75 and confirmed many novel predicted "off-target" activities. Given the verified generalizability, the model was further applied to various scenarios for depicting the kinome-wide selectivity and the association with certain diseases. Overall, the computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring previously less studied kinases.read more
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Journal ArticleDOI
Transfer Learning for Drug Discovery
TL;DR: This perspective aims to provide an overview of transferLearning and related applications in drug discovery and give outlooks as to future development and application of transfer learning for drug discovery.
Journal ArticleDOI
ProteomicsDB: a multi-omics and multi-organism resource for life science research.
Patroklos Samaras,Tobias Schmidt,Martin Frejno,Siegfried Gessulat,Maria Reinecke,Maria Reinecke,Maria Reinecke,Anna Jarzab,Jana Zecha,Julia Mergner,Piero Giansanti,Hans-Christian Ehrlich,Stephan Aiche,Johannes Rank,Harald Kienegger,Helmut Krcmar,Bernhard Kuster,Mathias Wilhelm +17 more
TL;DR: A new service in ProteomicsDB is introduced which allows users to upload their own expression datasets and analyze them alongside with data stored in ProeomicsDB, and supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana.
Journal ArticleDOI
An up-to-date overview of computational polypharmacology in modern drug discovery.
TL;DR: A comprehensive update on the current state-of-the-art polypharmacology approaches and their applications is provided, focusing on those reports published after the 2017 review article.
Journal ArticleDOI
Discovery of Pyrazolo[3,4-d]pyridazinone Derivatives as Selective DDR1 Inhibitors via Deep Learning Based Design, Synthesis, and Biological Evaluation.
Xiaoqin Tan,Chunpu Li,Ruirui Yang,Ruirui Yang,Sen Zhao,Fei Li,Xutong Li,Lifan Chen,Xiaozhe Wan,Liu Xiaohong,Tianbiao Yang,Xiaochu Tong,Tingyang Xu,Rongrong Cui,Hualiang Jiang,Hualiang Jiang,Sulin Zhang,Hong Liu,Mingyue Zheng +18 more
TL;DR: In this article, a deep generative model, kinase selectivity screening and molecular docking were used to develop DDR1 inhibitor compound 2, which showed potent DDR1 inhibition profile (IC50 = 10.6 ± 1.9 nM) and excellent selectivity against a panel of 430 kinases (S (10) = 0.002 at 0.1 μM).
Journal ArticleDOI
Recent advances in drug repurposing using machine learning.
TL;DR: A brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison can be found in this article, where the authors highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
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TL;DR: The protein kinase complement of the human genome is catalogued using public and proprietary genomic, complementary DNA, and expressed sequence tag sequences to provide a starting point for comprehensive analysis of protein phosphorylation in normal and disease states and a detailed view of the current state of human genome analysis through a focus on one large gene family.
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