Hongming Chen

According to our database, Hongming Chen authored at least 32 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning.

Hao Zhang, Jinchao Huang, Junjie Xie, Weifeng Huang, Yuedong Yang, Mingyuan Xu, Jinping Lei, Hongming Chen

J. Chem. Inf. Model., 2024

2023

An extensive benchmark study on biomedical text generation and mining with ChatGPT.

Qijie Chen, Haotong Sun, Haoyang Liu, Yinghui Jiang, Ting Ran, Xurui Jin, Xianglu Xiao, Zhimin Lin, Hongming Chen, Zhangming Niu

Bioinform., September, 2023

3D based generative PROTAC linker design with reinforcement learning.

Baiqing Li, Ting Ran, Hongming Chen

Briefings Bioinform., September, 2023

Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction.

Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen

CoRR, 2023

2022

Accelerated rational PROTAC design via deep learning and molecular simulations.

Shuangjia Zheng, Youhai Tan, Zhenyu Wang, Chengtao Li, Zhiqing Zhang, Xu Sang, Hongming Chen, Yuedong Yang

Nat. Mac. Intell., September, 2022

<i>De Novo</i> Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions.

Jie Zhang, Hongming Chen

J. Chem. Inf. Model., 2022

Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction.

Penglei Wang, Shuangjia Zheng, Yize Jiang, Chengtao Li, Junhong Liu, Chang Wen, Atanas Patronov, Dahong Qian, Hongming Chen, Yuedong Yang

J. Chem. Inf. Model., 2022

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design.

Youhai Tan, Lingxue Dai, Weifeng Huang, Yinfeng Guo, Shuangjia Zheng, Jinping Lei, Hongming Chen, Yuedong Yang

J. Chem. Inf. Model., 2022

2021

Graph networks for molecular design.

Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, Esben Jannik Bjerrum

Mach. Learn. Sci. Technol., 2021

Comparative Study of Deep Generative Models on Chemical Space Coverage.

Jie Zhang, Rocío Mercado, Ola Engkvist, Hongming Chen

J. Chem. Inf. Model., 2021

De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites.

Mingyuan Xu, Ting Ran, Hongming Chen

J. Chem. Inf. Model., 2021

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches.

Lizhao Hu, Yuyao Yang, Shuangjia Zheng, Jun Xu, Ting Ran, Hongming Chen

J. Chem. Inf. Model., 2021

Deep scaffold hopping with multimodal transformer neural networks.

Shuangjia Zheng, Zengrong Lei, Haitao Ai, Hongming Chen, Daiguo Deng, Yuedong Yang

J. Cheminformatics, 2021

2020

Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks.

Panagiotis-Christos Kotsias, Josep Arús-Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, Esben Jannik Bjerrum

Nat. Mach. Intell., 2020

REINVENT 2.0: An AI Tool for De Novo Drug Design.

Thomas Blaschke, Josep Arús-Pous, Hongming Chen, Christian Margreitter, Christian Tyrchan, Ola Engkvist, Kostas Papadopoulos, Atanas Patronov

J. Chem. Inf. Model., 2020

Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction.

Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen

J. Cheminformatics, 2020

Industry-scale application and evaluation of deep learning for drug target prediction.

Noé Sturm, Andreas Mayr, Thanh Le Van, Vladimir I. Chupakhin, Hugo Ceulemans, Jörg K. Wegner, José Felipe Golib Dzib, Nina Jeliazkova, Yves Vandriessche, Stanislav Böhm, Vojtech Cima, Jan Martinovic, Nigel Greene, Tom Vander Aa, Thomas J. Ashby, Sepp Hochreiter, Ola Engkvist, Günter Klambauer, Hongming Chen

J. Cheminformatics, 2020

SMILES-based deep generative scaffold decorator for de-novo drug design.

Josep Arús-Pous, Atanas Patronov, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist

J. Cheminformatics, 2020

2019

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, Günter Klambauer, Lars Carlsson, Ola Engkvist, Hongming Chen

J. Chem. Inf. Model., 2019

A de novo molecular generation method using latent vector based generative adversarial network.

Oleksii Prykhodko, Simon Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, Hongming Chen

J. Cheminformatics, 2019

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability.

Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, Ola Engkvist

J. Cheminformatics, 2019

Randomized SMILES strings improve the quality of molecular generative models.

Josep Arús-Pous, Simon Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist

J. Cheminformatics, 2019

Exploring the GDB-13 chemical space using deep generative models.

Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, Ola Engkvist

J. Cheminformatics, 2019

Attention and Edge Memory Convolution for Bioactivity Prediction.

Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

Improving Deep Generative Models with Randomized SMILES.

Josep Arús-Pous, Simon Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

2017

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

Jiangming Sun, Lars Carlsson, Ernst Ahlberg, Ulf Norinder, Ola Engkvist, Hongming Chen

J. Chem. Inf. Model., July, 2017

Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics.

Jiangming Sun, Nina Jeliazkova, Vladimir I. Chupakhin, José Felipe Golib Dzib, Ola Engkvist, Lars Carlsson, Jörg K. Wegner, Hugo Ceulemans, Ivan Georgiev, Vedrin Jeliazkov, Nikolay T. Kochev, Thomas J. Ashby, Hongming Chen

J. Cheminformatics, 2017

ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics.

Jiangming Sun, Nina Jeliazkova, Vladimir I. Chupakhin, José Felipe Golib Dzib, Ola Engkvist, Lars Carlsson, Jörg K. Wegner, Hugo Ceulemans, Ivan Georgiev, Vedrin Jeliazkov, Nikolay T. Kochev, Thomas J. Ashby, Hongming Chen

J. Cheminformatics, 2017

Molecular de-novo design through deep reinforcement learning.

Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen

J. Cheminformatics, 2017

Application of generative autoencoder in de novo molecular design.

Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen

CoRR, 2017

2010

Molecular Topology Analysis of the Differences between Drugs, Clinical Candidate Compounds, and Bioactive Molecules.

Hongming Chen, Yidong Yang, Ola Engkvist

J. Chem. Inf. Model., 2010

2009

ProSAR: A New Methodology for Combinatorial Library Design.

Hongming Chen, Ulf Borjesson, Ola Engkvist, Thierry Kogej, Mats Svensson, Niklas Blomberg, Dirk Weigelt, Jeremy N. Burrows, Tim Lange

J. Chem. Inf. Model., 2009