Deep generative design with 3D pharmacophoric constraints

Fergus Imrie, Thomas E. Hadfield, Anthony R. Bradley and Charlotte M. Deane

Chem. Sci., 2021, 12, 14577
DOI: 10.1039/D1SC02436A

Sample efficient reinforcement learning with active learning for molecular design

Michael Dodds, Jeff Guo, Thomas Löhr, Alessandro Tibo, Ola Engkvist and Jon Paul Janet

Chem. Sci., 2024, 15, 4146
DOI: 10.1039/D3SC04653B

A comprehensive review of emerging approaches in machine learning forde novoPROTAC design

Yossra Gharbi and Rocío Mercado

Digital Discovery, 2024, 3, 2158
DOI: 10.1039/D4DD00177J

Recent advances from computer-aided drug design to artificial intelligence drug design

Keran Wang, Yanwen Huang, Yan Wang, Qidong You and Lei Wang

RSC Med. Chem., 2024, 15, 3978
DOI: 10.1039/D4MD00522H

Link-INVENT: generative linker design with reinforcement learning

Jeff Guo, Franziska Knuth, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist and Atanas Patronov

Digital Discovery, 2023, 2, 392
DOI: 10.1039/D2DD00115B

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

Zuqin Wang, Ting Ran, Fang Xu, Chang Wen, Shukai Song, Yang Zhou, Hongming Chen and Xiaoyun Lu

Chem. Commun., 2021, 57, 10588
DOI: 10.1039/D1CC03392A

Meta Learning for Low-Resource Molecular Optimization

Jiahao Wang, Shuangjia Zheng, Jianwen Chen and Yuedong Yang

J. Chem. Inf. Model., 2021, 61, 1627
DOI: 10.1021/acs.jcim.0c01416

DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space

Martin Šícho, Sohvi Luukkonen, Helle W. van den Maagdenberg, Linde Schoenmaker, Olivier J. M. Béquignon and Gerard J. P. van Westen

J. Chem. Inf. Model., 2023, 63, 3629
DOI: 10.1021/acs.jcim.3c00434

Interpretable PROTAC Degradation Prediction With Structure‐Informed Deep Ternary Attention Framework

Zhenglu Chen, Chunbin Gu, Shuoyan Tan, Xiaorui Wang, Yuquan Li, Mutian He, Ruiqiang Lu, Shijia Sun, Chang‐Yu Hsieh, Xiaojun Yao, Huanxiang Liu and Pheng‐Ann Heng

Advanced Science, 2025
DOI: 10.1002/advs.202508138

Design, synthesis and bio-evaluation of 2,5-disubstituted thiazole derivatives for potential treatment of acute myeloid leukemia through targeting CDK9

Sumeng Chen, Jindi Huang, Shipeng Zhang, Xinni Zheng, Hongming Chen, Tie-Gen Chen and Ling Wang

Bioorganic Chemistry, 2025, 160, 108436
DOI: 10.1016/j.bioorg.2025.108436

LS-MolGen: Ligand-and-Structure Dual-Driven Deep Reinforcement Learning for Target-Specific Molecular Generation Improves Binding Affinity and Novelty

Song Li, Chao Hu, Song Ke, Chenxing Yang, Jun Chen, Yi Xiong, Hao Liu and Liang Hong

J. Chem. Inf. Model., 2023, 63, 4207
DOI: 10.1021/acs.jcim.3c00587

A comprehensive primer and review of PROTACs and their In Silico design

Jacopo Zattoni, Paola Vottero, Gea Carena, Chiara Uliveto, Giulia Pozzati, Benedetta Morabito, Ebenezea Gitari, Jack Tuszynski and Maral Aminpour

Computer Methods and Programs in Biomedicine, 2025, 264, 108687
DOI: 10.1016/j.cmpb.2025.108687

Structure-based design of anticancer drugs based on β-elemene: Research foundations and development potential

Haiyi Chen, Yuntao Yu, Chenghong Hu, Lehuang Zhou, Zhe Wang, Odin Zhang, Yi Wang and Tian Xie

Journal of Pharmaceutical Analysis, 2025, 101325
DOI: 10.1016/j.jpha.2025.101325

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy

Xing-Xing Shi, Zhi-Zheng Wang, Fan Wang, Ge-Fei Hao and Guang-Fu Yang

Nucleic Acids Research, 2023, 51, W25
DOI: 10.1093/nar/gkad348

Advances in designing ternary complexes: Integrating in-silico and biochemical methods for PROTAC optimisation in target protein degradation

Shareef Shaik, Prasanna Kumar Reddy Gayam, Manish Chaudhary, Gurvinder Singh and Aravinda Pai

Bioorganic Chemistry, 2024, 153, 107868
DOI: 10.1016/j.bioorg.2024.107868

3D based generative PROTAC linker design with reinforcement learning

Baiqing Li, Ting Ran and Hongming Chen

Briefings in Bioinformatics, 2023, 24
DOI: 10.1093/bib/bbad323

Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products

Chuan-Su Liu, Bing-Chao Yan, Han-Dong Sun, Jin-Cai Lu and Pema-Tenzin Puno

Nat. Prod. Bioprospect., 2025, 15
DOI: 10.1007/s13659-025-00521-y

DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model

Junjie Xie, Sheng Chen, Jinping Lei and Yuedong Yang

J. Chem. Inf. Model., 2024, 64, 2554
DOI: 10.1021/acs.jcim.3c01466

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

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

J. Chem. Inf. Model., 2021, 61, 4900
DOI: 10.1021/acs.jcim.1c00608

Scaffold Hopping with Generative Reinforcement Learning

Luke Rossen, Finton Sirockin, Nadine Schneider and Francesca Grisoni

J. Chem. Inf. Model., 2025, 65, 6513
DOI: 10.1021/acs.jcim.5c00029

Deep scaffold hopping with multimodal transformer neural networks

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

J Cheminform, 2021, 13
DOI: 10.1186/s13321-021-00565-5

Fragment‐based deep molecular generation using hierarchical chemical graph representation and multi‐resolution graph variational autoencoder

Zhenxiang Gao, Xinyu Wang, Blake Blumenfeld Gaines, Xuetao Shi, Jinbo Bi and Minghu Song

Molecular Informatics, 2023, 42
DOI: 10.1002/minf.202200215

Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning

Shogo Nakamura, Nobuaki Yasuo and Masakazu Sekijima

Commun Chem, 2025, 8
DOI: 10.1038/s42004-025-01437-x

PROTAC-Based Antivirals for Respiratory Viruses: A Novel Approach for Targeted Therapy and Vaccine Development

Amith Anugu, Pankaj Singh, Dharambir Kashyap, Jillwin Joseph, Sheetal Naik, Subhabrata Sarkar, Kamran Zaman, Manpreet Dhaliwal, Shubham Nagar, Tanishq Gupta and Prasanna Honnavar

Microorganisms, 2025, 13, 1557
DOI: 10.3390/microorganisms13071557

Open-Source Machine Learning in Computational Chemistry

Alexander Hagg and Karl N. Kirschner

J. Chem. Inf. Model., 2023, 63, 4505
DOI: 10.1021/acs.jcim.3c00643

Artificial intelligence in drug development for delirium and Alzheimer’s disease

Ruixue Ai, Xianglu Xiao, Shenglong Deng, Nan Yang, Xiaodan Xing, Leiv Otto Watne, Geir Selbæk, Yehani Wedatilake, Chenglong Xie, David C. Rubinsztein, Jennifer E. Palmer, Bjørn Erik Neerland, Hongming Chen, Zhangming Niu, Guang Yang and Evandro Fei Fang

Acta Pharmaceutica Sinica B, 2025, 15, 4386
DOI: 10.1016/j.apsb.2025.04.026

Accelerating discovery of bioactive ligands with pharmacophore-informed generative models

Weixin Xie, Jianhang Zhang, Qin Xie, Chaojun Gong, Yuhao Ren, Jin Xie, Qi Sun, Youjun Xu, Luhua Lai and Jianfeng Pei

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-56349-0

Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery

Yanyan Diao, Dandan Liu, Huan Ge, Rongrong Zhang, Kexin Jiang, Runhui Bao, Xiaoqian Zhu, Hongjie Bi, Wenjie Liao, Ziqi Chen, Kai Zhang, Rui Wang, Lili Zhu, Zhenjiang Zhao, Qiaoyu Hu and Honglin Li

Nat Commun, 2023, 14
DOI: 10.1038/s41467-023-40219-8

A Novel Rational PROTACs Design and Validation via AI-Driven Drug Design Approach

Cheng-Li Chou, Chieh-Te Lin, Chien-Ting Kao and Chu-Chung Lin

ACS Omega, 2024, 9, 38371
DOI: 10.1021/acsomega.3c10183

Artificial intelligence in molecular de novo design: Integration with experiment

Jon Paul Janet, Lewis Mervin and Ola Engkvist

Current Opinion in Structural Biology, 2023, 80, 102575
DOI: 10.1016/j.sbi.2023.102575

FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization

Jieyu Jin, Dong Wang, Guqin Shi, Jingxiao Bao, Jike Wang, Haotian Zhang, Peichen Pan, Dan Li, Xiaojun Yao, Huanxiang Liu, Tingjun Hou and Yu Kang

J. Med. Chem., 2023, 66, 10808
DOI: 10.1021/acs.jmedchem.3c01009

CMGN: a conditional molecular generation net to design target-specific molecules with desired properties

Minjian Yang, Hanyu Sun, Xue Liu, Xi Xue, Yafeng Deng and Xiaojian Wang

Briefings in Bioinformatics, 2023, 24
DOI: 10.1093/bib/bbad185

Rational Design of Covalent Kinase Inhibitors by an Integrated Computational Workflow (Kin-Cov)

Yang Zhou, Hang Yu, Anna Constance Vind, Lulu Kong, Yiling Liu, Xiaojuan Song, Zhengchao Tu, Caihong Yun, Jeff B. Smaill, Qing-Wen Zhang, Ke Ding, Simon Bekker-Jensen and Xiaoyun Lu

J. Med. Chem., 2023, 66, 7405
DOI: 10.1021/acs.jmedchem.3c00088

Molecular fragmentation as a crucial step in the AI-based drug development pathway

Shao Jinsong, Jia Qifeng, Chen Xing, Yajie Hao and Li Wang

Commun Chem, 2024, 7
DOI: 10.1038/s42004-024-01109-2

The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry

Yan Ivanenkov, Bogdan Zagribelnyy, Alex Malyshev, Sergei Evteev, Victor Terentiev, Petrina Kamya, Dmitry Bezrukov, Alex Aliper, Feng Ren and Alex Zhavoronkov

ACS Med. Chem. Lett., 2023, 14, 901
DOI: 10.1021/acsmedchemlett.3c00041

Fragment-based drug design facilitates selective kinase inhibitor discovery

Zhi-Zheng Wang, Xing-Xing Shi, Guang-Yi Huang, Ge-Fei Hao and Guang-Fu Yang

Trends in Pharmacological Sciences, 2021, 42, 551
DOI: 10.1016/j.tips.2021.04.001

Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer

Zhirui Liao, Lei Xie, Hiroshi Mamitsuka, Shanfeng Zhu and Jonathan Wren

Bioinformatics, 2023, 39
DOI: 10.1093/bioinformatics/btac814

General Theory of Fragment Linking in Molecular Design: Why Fragment Linking Rarely Succeeds and How to Improve Outcomes

Haoyu S. Yu, Kalyan Modugula, Osamu Ichihara, Kimberly Kramschuster, Simon Keng, Robert Abel and Lingle Wang

J. Chem. Theory Comput., 2021, 17, 450
DOI: 10.1021/acs.jctc.0c01004

Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking

Merveille Eguida, Christel Schmitt-Valencia, Marcel Hibert, Pascal Villa and Didier Rognan

J. Med. Chem., 2022, 65, 13771
DOI: 10.1021/acs.jmedchem.2c00931

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

Barmak Mostofian, Holli-Joi Martin, Asghar Razavi, Shivam Patel, Bryce Allen, Woody Sherman and Jesus A Izaguirre

J. Chem. Inf. Model., 2023, 63, 5408
DOI: 10.1021/acs.jcim.3c00603

Accelerating the design and development of polymeric materials via deep learning: Current status and future challenges

Dazi Li, Yi Ru, Zhudan Chen, Caibo Dong, Yining Dong and Jun Liu

APL Machine Learning, 2023, 1
DOI: 10.1063/5.0131067

Equivariant 3D-conditional diffusion model for molecular linker design

Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein and Bruno Correia

Nat Mach Intell, 2024, 6, 417
DOI: 10.1038/s42256-024-00815-9

A structure-based framework for selective inhibitor design and optimization

Yurong Zou, Tao Guo, Zhiyuan Fu, Zhongning Guo, Weichen Bo, Dengjie Yan, Qiantao Wang, Jun Zeng, Dingguo Xu, Taijin Wang and Lijuan Chen

Commun Biol, 2025, 8
DOI: 10.1038/s42003-025-07840-3

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Suvendu Nandi, Soumyadeep Bhaduri, Debraj Das, Priya Ghosh, Mahitosh Mandal and Pralay Mitra

Mol. Pharmaceutics, 2024, 21, 1563
DOI: 10.1021/acs.molpharmaceut.3c01161

Deep Lead Optimization: Leveraging Generative AI for Structural Modification

Odin Zhang, Haitao Lin, Hui Zhang, Huifeng Zhao, Yufei Huang, Chang-Yu Hsieh, Peichen Pan and Tingjun Hou

J. Am. Chem. Soc., 2024, 146, 31357
DOI: 10.1021/jacs.4c11686

STNGS: a deep scaffold learning-driven generation and screening framework for discovering potential novel psychoactive substances

Dongping Liu, Dinghao Liu, Kewei Sheng, Zhenyong Cheng, Zixuan Liu, Yanling Qiao, Shangxuan Cai, Yulong Li, Jubo Wang, Hongyang Chen, Chi Hu, Peng Xu, Bin Di and Jun Liao

Briefings in Bioinformatics, 2024, 26
DOI: 10.1093/bib/bbae690

Accelerated rational PROTAC design via deep learning and molecular simulations

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

Nat Mach Intell, 2022, 4, 739
DOI: 10.1038/s42256-022-00527-y

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade

Liuying Wang, Yongzhen Song, Hesong Wang, Xuan Zhang, Meng Wang, Jia He, Shuang Li, Liuchao Zhang, Kang Li and Lei Cao

Pharmaceuticals, 2023, 16, 253
DOI: 10.3390/ph16020253

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

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

J. Chem. Inf. Model., 2022, 62, 5907
DOI: 10.1021/acs.jcim.2c00982

Fragment-based drug discovery supports drugging ‘undruggable’ protein–protein interactions

Zhi-Zheng Wang, Xing-Xing Shi, Guang-Yi Huang, Ge-Fei Hao and Guang-Fu Yang

Trends in Biochemical Sciences, 2023, 48, 539
DOI: 10.1016/j.tibs.2023.01.008

Bridging the Gap between Transformer-Based Neural Networks and Tensor Networks for Quantum Chemistry

Bowen Kan, Yingqi Tian, Yangjun Wu, Yunquan Zhang and Honghui Shang

J. Chem. Theory Comput., 2025, 21, 3426
DOI: 10.1021/acs.jctc.4c01703

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search

Stephanie Wills, Ruben Sanchez-Garcia, Tim Dudgeon, Stephen D. Roughley, Andy Merritt, Roderick E. Hubbard, James Davidson, Frank von Delft and Charlotte M. Deane

J. Chem. Inf. Model., 2023, 63, 3423
DOI: 10.1021/acs.jcim.3c00276

Tree-Invent: A Novel Multipurpose Molecular Generative Model Constrained with a Topological Tree

Mingyuan Xu and Hongming Chen

J. Chem. Inf. Model., 2023, 63, 7067
DOI: 10.1021/acs.jcim.3c01626

Chemical language models for molecular design

Jürgen Bajorath

Molecular Informatics, 2024, 43
DOI: 10.1002/minf.202300288

Linkers in fragment-based drug design: an overview of the literature

Dylan Grenier, Solène Audebert, Jordane Preto, Jean-François Guichou and Isabelle Krimm

Expert Opinion on Drug Discovery, 2023, 18, 987
DOI: 10.1080/17460441.2023.2234285

SyntaLinker-Hybrid: A deep learning approach for target specific drug design

Yu Feng, Yuyao Yang, Wenbin Deng, Hongming Chen and Ting Ran

Artificial Intelligence in the Life Sciences, 2022, 2, 100035
DOI: 10.1016/j.ailsci.2022.100035

Rational Proteolysis Targeting Chimera Design Driven by Molecular Modeling and Machine Learning

Shuoyan Tan, Zhenglu Chen, Ruiqiang Lu, Huanxiang Liu and Xiaojun Yao

WIREs Comput Mol Sci, 2025, 15
DOI: 10.1002/wcms.70013

LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design

Vendy Fialková, Jiaxi Zhao, Kostas Papadopoulos, Ola Engkvist, Esben Jannik Bjerrum, Thierry Kogej and Atanas Patronov

J. Chem. Inf. Model., 2022, 62, 2046
DOI: 10.1021/acs.jcim.1c00469

Ugropy: An Extensible Python Package for Thermodynamic Model Functional Group Identification via Mathematical Optimization

Salvador E. Brandolín, Federico E. Benelli, Ivana Magario and José A. Scilipoti

Ind. Eng. Chem. Res., 2025, 64, 17217
DOI: 10.1021/acs.iecr.5c02552

Targeted protein degradation in cancers: Orthodox PROTACs and beyond

Jin Li, Xinxin Chen, Aiping Lu and Chao Liang

The Innovation, 2023, 4, 100413
DOI: 10.1016/j.xinn.2023.100413

A systematic review of deep learning chemical language models in recent era

Hector Flores-Hernandez and Emmanuel Martinez-Ledesma

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00916-y

A pharmacophore-guided deep learning approach for bioactive molecular generation

Huimin Zhu, Renyi Zhou, Dongsheng Cao, Jing Tang and Min Li

Nat Commun, 2023, 14
DOI: 10.1038/s41467-023-41454-9

FSscore: A Personalized Machine Learning‐Based Synthetic Feasibility Score

Rebecca M. Neeser, Bruno Correia and Philippe Schwaller

Chemistry Methods, 2024, 4
DOI: 10.1002/cmtd.202400024

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 and Hongming Chen

J. Chem. Inf. Model., 2024, 64, 666
DOI: 10.1021/acs.jcim.3c01700

Rationalizing PROTAC-Mediated Ternary Complex Formation Using Rosetta

Nan Bai, Sven A. Miller, Grigorii V. Andrianov, Max Yates, Palani Kirubakaran and John Karanicolas

J. Chem. Inf. Model., 2021, 61, 1368
DOI: 10.1021/acs.jcim.0c01451

DenovoProfiling: A webserver for de novo generated molecule library profiling

Zhihong Liu, Jiewen Du, Ziying Lin, Ze Li, Bingdong Liu, Zongbin Cui, Jiansong Fang and Liwei Xie

Computational and Structural Biotechnology Journal, 2022, 20, 4082
DOI: 10.1016/j.csbj.2022.07.045

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation

Pir Masoom Shah, Huimin Zhu, Zhangli Lu, Kaili Wang, Jing Tang and Min Li

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-59917-6

Computer-Aided Identification of Kinase-Targeted Small Molecules for Cancer: A Review on AKT Protein

Erika Primavera, Deborah Palazzotti, Maria Letizia Barreca and Andrea Astolfi

Pharmaceuticals, 2023, 16, 993
DOI: 10.3390/ph16070993

Generative Deep Learning for Targeted Compound Design

Tiago Sousa, João Correia, Vítor Pereira and Miguel Rocha

J. Chem. Inf. Model., 2021, 61, 5343
DOI: 10.1021/acs.jcim.0c01496

Transforming molecular cores, substituents, and combinations into structurally diverse compounds using chemical language models

Lisa Piazza, Sanjana Srinivasan, Tiziano Tuccinardi and Jürgen Bajorath

European Journal of Medicinal Chemistry, 2025, 291, 117615
DOI: 10.1016/j.ejmech.2025.117615

A Multi-Split-and-Mix Platform for a Variety of Targeted Protein Degradation

Yuechen Wang, Qinhong Luo, Yun Xing, Rui Wang, Fenfang Yang, Weifan Cao, Zigang Li and Feng Yin

ACS Chem. Biol., 2025, 20, 2287
DOI: 10.1021/acschembio.5c00444

DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning

Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00694-z

Fragment Linker Prediction Using the Deep Encoder-Decoder Network for PROTACs Drug Design

Chien-Ting Kao, Chieh-Te Lin, Cheng-Li Chou and Chu-Chung Lin

J. Chem. Inf. Model., 2023, 63, 2918
DOI: 10.1021/acs.jcim.2c01287

Generative models for molecular discovery: Recent advances and challenges

Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay and Klavs F. Jensen

WIREs Comput Mol Sci, 2022, 12
DOI: 10.1002/wcms.1608

A Review on Deep Learning-driven Drug Discovery: Strategies, Tools and Applications

Sundaravadivelu Sumathi, Kanagaraj Suganya, Kandasamy Swathi, Balraj Sudha, Arumugam Poornima, Chalos Angel Varghese and Raghu Aswathy

CPD, 2023, 29, 1013
DOI: 10.2174/1381612829666230412084137

Computational methods and key considerations for in silico design of proteolysis targeting chimera (PROTACs)

Amr Abbas and Fei Ye

International Journal of Biological Macromolecules, 2024, 277, 134293
DOI: 10.1016/j.ijbiomac.2024.134293

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

Qifeng Bai, Shuo Liu, Yanan Tian, Tingyang Xu, Antonio Jesús Banegas‐Luna, Horacio Pérez‐Sánchez, Junzhou Huang, Huanxiang Liu and Xiaojun Yao

WIREs Comput Mol Sci, 2022, 12
DOI: 10.1002/wcms.1581

The Impact of Computational Drug Discovery on Society

Jianxin Wang, Min Li, Edwin Wang, Jing Tang and Bin Hu

IEEE Trans. Comput. Soc. Syst., 2023, 10, 2148
DOI: 10.1109/TCSS.2023.3313208

ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks

Chao Hu, Song Li, Chenxing Yang, Jun Chen, Yi Xiong, Guisheng Fan, Hao Liu and Liang Hong

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00766-0

PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models

Morgan Thomas, Mazen Ahmad, Gary Tresadern and Gianni de Fabritiis

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00866-5

In silico modeling of targeted protein degradation

Wenxing Lv, Xiaojuan Jia, Bowen Tang, Chao Ma, Xiaopeng Fan, Xurui Jin, Zhangming Niu and Xin Han

European Journal of Medicinal Chemistry, 2025, 289, 117432
DOI: 10.1016/j.ejmech.2025.117432

Applications of Deep Learning for Drug Discovery Systems with BigData

Yasunari Matsuzaka and Ryu Yashiro

BioMedInformatics, 2022, 2, 603
DOI: 10.3390/biomedinformatics2040039

Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design

Yang Yu, Junhong Huang, Hu He, Jing Han, Geyan Ye, Tingyang Xu, Xianqiang Sun, Xiumei Chen, Xiaoming Ren, Chunlai Li, Huijuan Li, Wei Huang, Yangyang Liu, Xinjuan Wang, Yongzhi Gao, Nianhe Cheng, Na Guo, Xibo Chen, Jianxia Feng, Yuxia Hua, Chong Liu, Guoyun Zhu, Zhi Xie, Lili Yao, Wenge Zhong, Xinde Chen, Wei Liu and Hailong Li

ACS Med. Chem. Lett., 2023, 14, 297
DOI: 10.1021/acsmedchemlett.2c00515

TRL-PROTAC: A pre-trained generator of PROTACs targeting specific proteins optimized by reinforcement learning

Yuhao Dai and Fei Zhu

ComSIS, 2024, 21, 1293
DOI: 10.2298/CSIS240327039D

Evaluation of reinforcement learning in transformer-based molecular design

Jiazhen He, Alessandro Tibo, Jon Paul Janet, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky and Ola Engkvist

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00887-0

Application of deep generative model for design of Pyrrolo[2,3-d] pyrimidine derivatives as new selective TANK binding kinase 1 (TBK1) inhibitors

Shukai Song, Haotian Tang, Ting Ran, Feng Fang, Linjiang Tong, Hongming Chen, Hua Xie and Xiaoyun Lu

European Journal of Medicinal Chemistry, 2023, 247, 115034
DOI: 10.1016/j.ejmech.2022.115034