Deep learning-driven scaffold hopping in the discovery of Akt kinase inhibitors

2021 ◽  
Vol 57 (81) ◽  
pp. 10588-10591
Author(s):  
Zuqin Wang ◽  
Ting Ran ◽  
Fang Xu ◽  
Chang Wen ◽  
Shukai Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Scaffold Hopping ◽  
High Potency ◽  
Akt Kinase

A deep conditional transformer neural network, SyntaLinker, was used for scaffold hopping in the discovery of Akt inhibitors. A novel Akt kinase inhibitor 1d with high potency (Akt1 IC50 = 88 nM, U937 IC50 = 0.39 μM) was discovered.

scholarly journals A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Feiqi Wang ◽  
Yun-Ti Chen ◽  
Jinn-Moon Yang ◽  
Tatsuya Akutsu
Keyword(s):  
Neural Network ◽  
Protein Kinase ◽  
Convolutional Neural Network ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Protein Kinase Inhibitors ◽  
Superior Performance ◽  
Interaction Sites ◽  
Mechanism Research ◽  
Ablation Study

AbstractProtein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI.

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

2021 ◽  
Author(s):  
Lizhao Hu ◽  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Jun Xu ◽  
Ting Ran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Kinase Inhibitor ◽  
Learning Approaches ◽  
Scaffold Hopping

scholarly journals A Deep Learning Based Scaffold Hopping Strategy for the Design of Kinase Inhibitors

2021 ◽  
Author(s):  
Lizhao Hu ◽  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Jun Xu ◽  
Ting Ran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Large Scale ◽  
Kinase Inhibitors ◽  
Learning Strategy ◽  
Binding Motif ◽  
Scaffold Hopping

Scaffold hopping is a widely used strategy for drug design towards kinase inhibitors. In current study, we proposed a fragment-based deep learning strategy for scaffold hopping towards the conserved hinge binding motif of kinase inhibitors in a large scale.

scholarly journals A Deep Learning Based Scaffold Hopping Strategy for the Design of Kinase Inhibitors

2021 ◽  
Author(s):  
Lizhao Hu ◽  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Jun Xu ◽  
Ting Ran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Large Scale ◽  
Kinase Inhibitors ◽  
Learning Strategy ◽  
Binding Motif ◽  
Scaffold Hopping

Scaffold hopping is a widely used strategy for drug design towards kinase inhibitors. In current study, we proposed a fragment-based deep learning strategy for scaffold hopping towards the conserved hinge binding motif of kinase inhibitors in a large scale.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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