ProSAR: A New Methodology for Combinatorial Library Design. - Hongming Chen 001; Ulf Borjesson; Ola Engkvist; Thierry Kogej; Mats Svensson; Niklas Blomberg; Dirk Weigelt; Jeremy N. Burrows; Tim Lange 002 (J. Chem. Inf. Model., 2009)

Molecular Topology Analysis of the Differences between Drugs, Clinical Candidate Compounds, and Bioactive Molecules. - Hongming Chen 001; Yidong Yang; Ola Engkvist (J. Chem. Inf. Model., 2010)

Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms. - Hongming Chen 001; Lars Carlsson; Mats Eriksson; Péter L. Várkonyi; Ulf Norinder; Ingemar Nilsson (J. Chem. Inf. Model., 2013)

Application of generative autoencoder in de novo molecular design. - Thomas Blaschke; Marcus Olivecrona; Ola Engkvist; Jürgen Bajorath; Hongming Chen 001 (CoRR, 2017)

Molecular de-novo design through deep reinforcement learning. - Marcus Olivecrona; Thomas Blaschke; Ola Engkvist; Hongming Chen 001 (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; et al. (J. Cheminformatics, 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; et al. (J. Cheminformatics, 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 001 (J. Chem. Inf. Model., 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; Lars Carlsson; Jörg K. Wegner; Hugo Ceulemans; Ivan Georgiev; Vedrin Jeliazkov; Nikolay T. Kochev; Thomas J. Ashby; Hongming Chen 001 (BNAIC/BENELEARN, 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 001; Ola Engkvist (ICANN (Workshop), 2019)

Attention and Edge Memory Convolution for Bioactivity Prediction. - Michael Withnall; Edvard Lindelöf; Ola Engkvist; Hongming Chen 001 (ICANN (Workshop), 2019)

Exploring the GDB-13 chemical space using deep generative models. - Josep Arús-Pous; Thomas Blaschke; Silas Ulander; Jean-Louis Reymond; Hongming Chen 001; 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 001; Ola Engkvist (J. Cheminformatics, 2019)

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. - Oliver Laufkötter; Noé Sturm; Jürgen Bajorath; Hongming Chen 001; Ola Engkvist (J. Cheminformatics, 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 001 (J. Cheminformatics, 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 001 (J. Chem. Inf. Model., 2019)

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 001; Ola Engkvist (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; et al. (J. Cheminformatics, 2020)

Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction. - Michael Withnall; Edvard Lindelöf; Ola Engkvist; Hongming Chen 001 (J. Cheminformatics, 2020)

REINVENT 2.0: An AI Tool for De Novo Drug Design. - Thomas Blaschke; Josep Arús-Pous; Hongming Chen 001; Christian Margreitter; Christian Tyrchan; Ola Engkvist; Kostas Papadopoulos; Atanas Patronov (J. Chem. Inf. Model., 2020)

Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. - Panagiotis-Christos Kotsias; Josep Arús-Pous; Hongming Chen 001; Ola Engkvist; Christian Tyrchan; Esben Jannik Bjerrum (Nat. Mach. Intell., 2020)

Deep scaffold hopping with multimodal transformer neural networks. - Shuangjia Zheng; Zengrong Lei; Haitao Ai; Hongming Chen 001; Daiguo Deng; Yuedong Yang (J. Cheminformatics, 2021)

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches. - Lizhao Hu; Yuyao Yang; Shuangjia Zheng; Jun Xu 017; Ting Ran; Hongming Chen 001 (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 001 (J. Chem. Inf. Model., 2021)

Comparative Study of Deep Generative Models on Chemical Space Coverage. - Jie Zhang 089; Rocío Mercado; Ola Engkvist; Hongming Chen 001 (J. Chem. Inf. Model., 2021)

Graph networks for molecular design. - Rocío Mercado; Tobias Rastemo; Edvard Lindelöf; Günter Klambauer; Ola Engkvist; Hongming Chen 001; Esben Jannik Bjerrum (Mach. Learn. Sci. Technol., 2021)

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design. - Youhai Tan; Lingxue Dai; Weifeng Huang; Yinfeng Guo; Shuangjia Zheng; Jinping Lei; Hongming Chen 001; Yuedong Yang (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 001; Yuedong Yang (J. Chem. Inf. Model., 2022)

De Novo Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions. - Jie Zhang 089; Hongming Chen 001 (J. Chem. Inf. Model., 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 001; Yuedong Yang (Nat. Mac. Intell., 2022)

Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction. - Zhiguang Fan; Yuedong Yang; Mingyuan Xu; Hongming Chen 001 (CoRR, 2023)

3D based generative PROTAC linker design with reinforcement learning. - Baiqing Li; Ting Ran; Hongming Chen 001 (Briefings Bioinform., 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 001; Zhangming Niu (Bioinform., 2023)

How Good are Current Pocket-Based 3D Generative Models?: The Benchmark Set and Evaluation of Protein Pocket-Based 3D Molecular Generative Models. - Haoyang Liu; Yifei Qin; Zhangming Niu; Mingyuan Xu; Jiaqiang Wu; Xianglu Xiao; Jinping Lei; Ting Ran; Hongming Chen 001 (J. Chem. Inf. Model., 2024)

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning. - Hao Zhang; Jinchao Huang 004; Junjie Xie; Weifeng Huang; Yuedong Yang; Mingyuan Xu; Jinping Lei; Hongming Chen 001 (J. Chem. Inf. Model., 2024)

EC-Conf: A ultra-fast diffusion model for molecular conformation generation with equivariant consistency. - Zhiguang Fan; Yuedong Yang; Mingyuan Xu; Hongming Chen 001 (J. Cheminformatics, 2024)

A 3D pocket-aware lead optimization model with knowledge guidance and its application for discovery of new glutaminyl cyclase inhibitors. - Anjie Qiao; Yuting Chen; Junjie Xie; Weifeng Huang; Hao Zhang; Qirui Deng; Jiahua Rao; Ji Deng; Fanbo Meng; Zhen Wang 004; Mingyuan Xu; Hongming Chen 001; et al. (Briefings Bioinform., 2025)

NA-DB: An Online Database of Nucleoside Analogues. - Haolin Luo; Ting Ran; Ling Wang 008; Hongming Chen 001 (J. Chem. Inf. Model., 2025)