List of Molecular and Material design (molecular conformation generation) using Generative AI and Deep Learning

related to Generative AI and Deep Learning for molecular/drug design and molecular conformation generation.

Molecular GenerativeAI

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Molecular Optimization

Molecular Optimization will welcome ！！！

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Unified Deep Molecular Generative Model
Generative AI for Scientific Discovery	Reviews	Datasets and Benchmarks	Drug-likeness and Evaluation metrics
Deep Learning-based design	Text-driven molecular generation models	Multi-Target based deep molecular generative models	Ligand-based deep molecular generative models
Pharmacophore-based deep molecular generative models	Structure-based deep molecular generative models	Fragment-based deep molecular generative models	Scaffold-based DMGs
Fragment-based DMGs	Motifs-based DMGs	Linkers-based DMGs	Chemical Reaction-based deep molecular generative models
Omics-based deep molecular generative models	Multi-Objective deep molecular generative models	Quantum deep molecular generative models	Recommendations and References
Spectra(Mass/NMR)-based	Mass Spectra-based	NMR Spectra-based	Cryo-EM Maps-based

Drug-likeness and Evaluation metrics

Datasets	Benchmarks	Drug-likeness	Evaluation metrics
Datasets	Benchmarks	QED	SAscore
		QEPPI	RAscore
			Evaluation metrics
			Molecular generative validation

Generative AI for Molecular Conformation

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Benchmark for Molecular Conformer Ensembles	Reviews for Molecular Conformation Generation
VAE-based Molecular Conformation Generation	GAN-based Molecular Conformation Generation
Energy-based Molecular Conformation Generation	Flow Matching-based Molecular Conformation Generation
Diffusion-based Molecular Conformation Generation
RL-based Molecular Conformation Generation
GNN-based Molecular Conformation Generation

Deep Learning-based drug design

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RNN-based	LSTM-based	Autoregressive-models	Transformer-based
VAE-based	GAN-based	Flow-based	Flow matching-based
Prompt-based	Score-Based	Energy-based	Diffusion-based
Active Learning DMGs	RL-based	Multi-task DMGs	Tree Search
Genetic Algorithm-based	Evolutionary Algorithm-based	Large Language Model-based	Agent-based

Material Design Using Generative Artificial Intelligence and Deep Learning

Deep Learning-based material design

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Recommendations and References

awesome-AI4ProteinConformation-MD

https://github.com/AspirinCode/awesome-AI4ProteinConformation-MD

Large Language Model for Biomedical Science, Molecule, Protein, Material Discovery

https://github.com/HHW-zhou/LLM4Mol

List of papers about Proteins Design using Deep Learning

https://github.com/Peldom/papers_for_protein_design_using_DL

Awesome Generative AI

https://github.com/steven2358/awesome-generative-ai

awesome-molecular-generation

https://github.com/amorehead/awesome-molecular-generation

A Survey of Artificial Intelligence in Drug Discovery

https://github.com/dengjianyuan/Survey_AI_Drug_Discovery

Geometry Deep Learning for Drug Discovery and Life Science

https://github.com/3146830058/Geometry-Deep-Learning-for-Drug-Discovery-and-Life-Science

Unified Deep Molecular Generative Model

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials [2025]
Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary W. Ulissi.
arXiv:2503.03965 (2025)
UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion [2025]
Zhang, Gongbo, Yanting Li, Renqian Luo, Pipi Hu, Zeru Zhao, Lingbo Li, Guoqing Liu et al.
arXiv:2503.06687 (2025)

Generative AI for Scientific Discovery

Accelerating Material Design with the Generative Toolkit for Scientific Discovery
Manica, Matteo and Cadow, Joris and Christofidellis, Dimitrios and Dave, Ashish and Born, Jannis and Clarke, Dean and Teukam, Yves Gaetan Nana and Hoffman, Samuel C and Buchan, Matthew and Chenthamarakshan, Vijil and others
npj Comput Mater 9, 69 (2023) | code

Reviews

From Computational Chemistry to Generative Models: A Survey of AI-Driven Small-Molecule Drug Discovery [2026]
Houman Kazemzadeh, Kiarash Mokhtari, Nazanin Mirzaei, et al.
ChemRxiv. (2026)
How Molecular Generative Models Organize Molecular Identity [2026]
Raul Ortega-Ochoa, Tejs Vegge, Jens S. Bakander, Luis Mantilla Calderon, Alan Aspuru-Guzik, Tonio Buonassisi.
arXiv:2608.06956(2026)
A comprehensive review of artificial intelligence in drug design: methods, applications, and challenges [2026]
Fang, Yi, Xiaoyong Pan, and Hong-Bin Shen.
Digital Discovery (2026)
Structure-Based Drug Design with Geometric Deep Learning: A Comprehensive Survey [2025]
Zhang, Zaixi and Yan, Jiaxian and Huang, Yining and Liu, Qi and Chen, Enhong and Wang, Mengdi and Zitnik, Marinka.
ACM Comput. Surv.(2025)
A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development [2025]
Saghir Ali, Xiaochen Tian, Haiying Chen, and Jia Zhou.
J. Med. Chem. (2025)
Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design [2025]
Vost, Lucy, Yael Ziv, and Charlotte Mary Deane.
Chem. Sci. (2025)
Structure-Based Generation of 3D Small-Molecule Drugs: Are We There Yet? [2025]
Bo Yang, Chijian Xiang, Tongtong Li, Yunong Xu, and Jianing Li.
J. Med. Chem. (2025) | code
Improving the Reliability of Molecular String Representations for Generative Chemistry [2025]
Etienne Reboul, Zoe Wefers, Harish Prabakaran, Jérôme Waldispühl, and Antoine Taly.
J. Chem. Inf. Model. (2025) | code
In Search of Beautiful Molecules: A Perspective on Generative Modeling for Drug Design [2025]
Remco L. van den Broek, Shivam Patel, Gerard J. P. van Westen, Willem Jespers, and Woody Sherman.
J. Chem. Inf. Model. (2025)
A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization [2025]
Ziqing Wang, Kexin Zhang, Zihan Zhao, Yibo Wen, Abhishek Pandey, Han Liu, Kaize Ding.
arXiv:2505.16094 (2025) | code
Generative deep learning for de novo drug design – a chemical space odyssey [2025]
Özçelik R, Brinkmann H, Criscuolo E, Grisoni F.
ChemRxiv. (2025)
Diffusion Models for Molecules: A Survey of Methods and Tasks [2025]
Liang Wang, Chao Song, Zhiyuan Liu, Yu Rong, Qiang Liu, Shu Wu, Liang Wang.
arXiv:2502.09511 (2025) | code
Machine learning-aided generative molecular design [2024]
Du, Y., Jamasb, A.R., Guo, J. et al.
Nat Mach Intell 6, 589–604 (2024)
Diffusion Models in De Novo Drug Design [2024]
Alakhdar, Amira, Barnabas Poczos, and Newell Washburn.
J. Chem. Inf. Model. (2024)
Navigating the frontier of drug-like chemical space with cutting-edge generative AI models [2024]
Zhang, Odin, Haitao Lin, Hui Zhang, Huifeng Zhao, Yufei Huang, Yuansheng Huang, Dejun Jiang, Chang-yu Hsieh, Peichen Pan, and Tingjun Hou.
Drug Discovery Today (2024)
Deep Lead Optimization: Leveraging Generative AI for Structural Modification [2024]
Zhang, Odin, Haitao Lin, Hui Zhang, Huifeng Zhao, Yufei Huang, Yuansheng Huang, Dejun Jiang, Chang-yu Hsieh, Peichen Pan, and Tingjun Hou.
J. Am. Chem. Soc. (2024) | arXiv:2404.19230 (2024)
Unlocking the Potential of Generative Artificial Intelligence in Drug Discovery [2024]
Romanelli, Virgilio, Carmen Cerchia, and Antonio Lavecchia.
Applications of Generative AI (2024)
Recent Advances in Automated Structure-Based De Novo Drug Design [2024]
Bai, Qifeng, Jian Ma, and Tingyang Xu.
J. Chem. Inf. Model. (2024)
A comprehensive review of molecular optimization in artificial intelligence‐based drug discovery [2024]
Xia, Yuhang, Yongkang Wang, Zhiwei Wang, and Wen Zhang.
Quantitative Biology 12.1 (2024)
AI Deep Learning Generative Models for Drug Discovery [2024]
Bai, Qifeng, Jian Ma, and Tingyang Xu.
Applications of Generative AI. Cham: Springer International Publishing (2024)
Deep Generative Models in De Novo Drug Molecule Generation [2024]
Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu, Yunyang Li, Tianxiao Li, Mark Gerstein
arXiv:2402.08703 (2024) | code
Deep Generative Models in De Novo Drug Molecule Generation [2023]
Chao Pang, Jianbo Qiao, Xiangxiang Zeng, Quan Zou, and Leyi Wei*
J. Chem. Inf. Model. (2023)
The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry [2023]
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)
Quantum computing for near-term applications in generative chemistry and drug discovery [2023]
Pyrkov, Alexey, Alex Aliper, Dmitry Bezrukov, Yen-Chu Lin, Daniil Polykovskiy, Petrina Kamya, Feng Ren, and Alex Zhavoronkov.
Drug Discovery Today (2023)
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design[2023]
Zaixi Zhang, Jiaxian Yan, Qi Liu, Enhong Chen
arXiv:2306.11768v2
How will generative AI disrupt data science in drug discovery?[2023]
Vert, JP.
Nat Biotechnol (2023)
Generative Models as an Emerging Paradigm in the Chemical Sciences[2023]
Anstine, Dylan M., and Olexandr Isayev.
JACS (2023)
Chemical language models for de novo drug design: Challenges and opportunities[2023]
Grisoni, Francesca.
Current Opinion in Structural Biology 79 (2023)
Artificial intelligence in multi-objective drug design[2023]
Luukkonen, Sohvi, Helle W. van den Maagdenberg, Michael TM Emmerich, and Gerard JP van Westen.
Current Opinion in Structural Biology 79 (2023)
Integrating structure-based approaches in generative molecular design[2023]
Thomas, Morgan, Andreas Bender, and Chris de Graaf.
Current Opinion in Structural Biology 79 (2023)
Open data and algorithms for open science in AI-driven molecular informatics[2023]
Brinkhaus, Henning Otto, Kohulan Rajan, Jonas Schaub, Achim Zielesny, and Christoph Steinbeck.
Current Opinion in Structural Biology 79 (2023)
Structure-based drug design with geometric deep learning[2023]
Isert, Clemens, Kenneth Atz, and Gisbert Schneider.
Current Opinion in Structural Biology 79 (2023)
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design[2022]
Du, Yuanqi, Tianfan Fu, Jimeng Sun, and Shengchao Liu.
arXiv:2203.14500 (2022)
Deep generative molecular design reshapes drug discovery[2022]
Zeng, Xiangxiang, Fei Wang, Yuan Luo, Seung-gu Kang, Jian Tang, Felice C. Lightstone, Evandro F. Fang, Wendy Cornell, Ruth Nussinov, and Feixiong Cheng.
Cell Reports Medicine (2022)
Structure-based drug discovery with deep learning[2022]
Özçelik, Rıza, Derek van Tilborg, José Jiménez-Luna, and Francesca Grisoni.
ChemBioChem (2022)
Generative models for molecular discovery: Recent advances and challenges[2022]
Bilodeau, Camille, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen.
Computational Molecular Science 12.5 (2022)
Assessing Deep Generative Models in Chemical Composition Space[2022]
Türk, Hanna, Elisabetta Landini, Christian Kunkel, Johannes T. Margraf, and Karsten Reuter.
Chemistry of Materials 34.21 (2022)
Generative machine learning for de novo drug discovery: A systematic review[2022]
Martinelli, Dominic.
Computers in Biology and Medicine 145 (2022)
Docking-based generative approaches in the search for new drug candidates[2022]
Danel, Tomasz, Jan Łęski, Sabina Podlewska, and Igor T. Podolak.
Drug Discovery Today (2022)
Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models[2022]
Xie, Weixin, Fanhao Wang, Yibo Li, Luhua Lai, and Jianfeng Pei.
J. Chem. Inf. Model. 2022, 62, 10, 2269–2279
Deep learning to catalyze inverse molecular design[2022]
Alshehri, Abdulelah S., and Fengqi You.
Chemical Engineering Journal 444 (2022)
AI in 3D compound design[2022]
Hadfield, Thomas E., and Charlotte M. Deane.
Current Opinion in Structural Biology 73 (2022)
Deep learning approaches for de novo drug design: An overview[2021]
Wang, Mingyang, Zhe Wang, Huiyong Sun, Jike Wang, Chao Shen, Gaoqi Weng, Xin Chai, Honglin Li, Dongsheng Cao, and Tingjun Hou.
Current Opinion in Structural Biology 72 (2022)
Generative chemistry: drug discovery with deep learning generative models[2021]
Bian, Yuemin, and Xiang-Qun Xie.
Journal of Molecular Modeling 27 (2021)
Generative Deep Learning for Targeted Compound Design[2021]
Sousa, Tiago, João Correia, Vítor Pereira, and Miguel Rocha.
J. Chem. Inf. Model. 2021, 61, 11, 5343–5361
Generative Models for De Novo Drug Design[2021]
Tong, Xiaochu, Xiaohong Liu, Xiaoqin Tan, Xutong Li, Jiaxin Jiang, Zhaoping Xiong, Tingyang Xu, Hualiang Jiang, Nan Qiao, and Mingyue Zheng.
Journal of Medicinal Chemistry 64.19 (2021)
Molecular design in drug discovery: a comprehensive review of deep generative models[2021]
Cheng, Yu, Yongshun Gong, Yuansheng Liu, Bosheng Song, and Quan Zou.
Briefings in bioinformatics 22.6 (2021)
De novo molecular design and generative models[2021]
Meyers, Joshua, Benedek Fabian, and Nathan Brown.
Drug Discovery Today 26.11 (2021)
Deep learning for molecular design—a review of the state of the art[2019]
Elton, Daniel C., Zois Boukouvalas, Mark D. Fuge, and Peter W. Chung.
Molecular Systems Design & Engineering 4.4 (2019)
Inverse molecular design using machine learning: Generative models for matter engineering[2018]
Sanchez-Lengeling, Benjamin, and Alán Aspuru-Guzik.
Science 361.6400 (2018)

Datasets and Benchmarks

Datasets

COCONUT | Collection of Open Natural Products database

MolData
A Molecular Benchmark for Disease and Target Based Machine Learning
https://github.com/LumosBio/MolData

Machine Learning Methods for Small Data Challenges in Molecular Science [2023]
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, Lu Ke, Long Chen, Jian Jiang, Yueying Zhu, Jie Liu, Bengong Zhang, and Guo-Wei Wei
Chem. Rev (2023)

Benchmarks

ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models [2026]
Yap, V., Xu, P., Mak, F.S. et al.
J Cheminform (2026) | code
Bento: A Comprehensive Benchmark for Protein-Ligand Docking Methods [2024]
LigandPro Team.
software
Benchmarking Study of Deep Generative Models for Inverse Polymer Design [2024]
Yue T, Tao L, Varshney V, Li Y.
chemrxiv-2024-gzq4r (2024)
RediscMol: Benchmarking Molecular Generation Models in Biological Properties [2024]
Weng, Gaoqi, Huifeng Zhao, Dou Nie, Haotian Zhang, Liwei Liu, Tingjun Hou, and Yu Kang.
J. Med. Chem. 2024 | code
Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark [2023]
Ciepliński, Tobiasz, Tomasz Danel, Sabina Podlewska, and Stanisław Jastrzȩbski.
J. Chem. Inf. Model. 2023, 63, 11, 3238–3247 | code
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design [2022]
Nigam, AkshatKumar, Robert Pollice, Gary Tom, Kjell Jorner, Luca A.
arXiv:2209.12487v1 | code
Molecular Sets (MOSES): A benchmarking platform for molecular generation models [2020]
Polykovskiy, Daniil, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov et al.
Frontiers in pharmacology 11 (2020) | code
GuacaMol: Benchmarking Models for de Novo Molecular Design [2019]
Brown, Nathan, Marco Fiscato, Marwin HS Segler, and Alain C. Vaucher.
J. Chem. Inf. Model. 2019, 59, 3, 1096–1108 | code

Drug-likeness and Evaluation metrics

Drug-likeness may be defined as a complex balance of various molecular properties and structure features which determine whether particular molecule is similar to the known drugs. These properties, mainly hydrophobicity, electronic distribution, hydrogen bonding characteristics, molecule size and flexibility and of course presence of various pharmacophoric features influence the behavior of molecule in a living organism, including bioavailability, transport properties, affinity to proteins, reactivity, toxicity, metabolic stability and many others.

https://github.com/AspirinCode/DrugAI_Drug-Likeness

QED

quantitative estimation of drug-likeness

Quantifying the chemical beauty of drugs [2012]
Bickerton, G., Paolini, G., Besnard, J. et al.
Nature Chem 4, 90–98 (2012) | code

QEPPI

quantitative estimate of protein-protein interaction targeting drug-likeness

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions [2021]
Kosugi, Takatsugu, and Masahito Ohue.
International Journal of Molecular Sciences 22.20 (2021) | code
Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness [2021]
Kosugi, Takatsugi, and Masahito Ohue.
CIBCB. IEEE, (2021) | code

SAscore

Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
J Cheminform 1, 8 (2009) | code

RAscore

Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning
Chemical Science 12.9 (2021) | code

SCScore

SCScore: Synthetic Complexity Learned from a Reaction Corpus
J. Chem. Inf. Model. (2018) | code

SYBA

SCScore: Synthetic Complexity Learned from a Reaction Corpus
J Cheminform 12, 35 (2020) | code

Evaluation metrics

MolScope: A Transparent Representation-Aware Workflow Toolkit for Reproducible Medicinal-Chemistry Triage [2026]
Elokely Khaled M.
Molecular Informatics (2026) | code
Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits [2024]
Hu, X., Liu, G., Yao, Q. et al.
J Cheminform 16, 94 (2024) | code
Spacial Score – A Comprehensive Topological Indicator for Small Molecule Complexity [2023]
Krzyzanowski, Adrian, Axel Pahl, Michael Grigalunas, and Herbert Waldmann.
J. Med. Chem. (2023) | chemrxiv-2023-nd1ll | code
An automated scoring function to facilitate and standardize evaluation of goal-directed generative models for de novo molecular design [2023]
Thomas, Morgan, Noel M. O'Boyle, Andreas Bender, and Chris De Graaf.
chemrxiv-2023-c4867 | code
FCD : Fréchet ChemNet Distance
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery Preuer, Kristina, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer.
J. Chem. Inf. Model. 2018, 58, 9, 1736–1741 | code
Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models [2022]
Moret, M., Grisoni, F., Katzberger, P. and Schneider, G.
J. Chem. Inf. Model. 2022, 62, 5, 1199–1206 | code

Molecular generative validation

On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data [2023]
Handa, K., Thomas, M.C., Kageyama, M. et al.
J Cheminform 15, 112 (2023)

Generative AI for Molecular Conformation

Reviews for Molecular Conformation Generation

Prediction of Molecular Conformation Using Deep Generative Neural Networks [2023]
Xu, Congsheng, Yi Lu, Xiaomei Deng, and Peiyuan Yu.
Chinese Journal of Chemistry(2023)

Benchmark for Molecular Conformer Ensembles

Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks [2023]
Zhu, Yanqiao, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du et al.
NeurIPS 2023 AI for Science Workshop. 2023 (2023) | code

VAE-based Molecular Conformation Generation

Deep-Learning-Assisted Enhanced Sampling for Exploring Molecular Conformational Changes [2023]
Haohao Fu, Han Liu, Jingya Xing, Tong Zhao, Xueguang Shao, and Wensheng Cai.
J. Phys. Chem. B (2023)
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming [2021]
Xu, Minkai, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, and Jian Tang.
International Conference on Machine Learning. PMLR (2021) | code

GAN-based Molecular Conformation Generation

Generation of molecular conformations using generative adversarial neural networks [2024]
Xu, Congsheng, Xiaomei Deng, Yi Lu, and Peiyuan Yu.
Digital Discovery (2024) | code
COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework [2024]
Kuznetsov, Maksim, Fedor Ryabov, Roman Schutski, Rim Shayakhmetov, Yen-Chu Lin, Alex Aliper, and Daniil Polykovskiy.
J. Chem. Inf. Model. (2024) | code

Energy-based Molecular Conformation Generation

Energy-inspired molecular conformation optimization [2022]
Guan, Jiaqi, Wesley Wei Qian, Wei-Ying Ma, Jianzhu Ma, and Jian Peng.
International Conference on Learning Representations. (2022) | code

Flow Matching-based Molecular Conformation Generation

Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization [2025]
Bergues, Noémie, Arthur Carré, Paul Join-Lambert, Brice Hoffmann, Arnaud Blondel, and Hamza Tajmouati..
arXiv:2506.06305 (2025) | code

Diffusion-based Molecular Conformation Generation

Scalable Low-Energy Molecular Conformer Generation with Quantum Mechanical Accuracy [2025]
Filipp Nikitin, Dylan M. Anstine, Roman Zubatyuk, et alFilipp Nikitin, Dylan M. Anstine, Roman Zubatyuk, et al.
ChemRxiv. (2025) | code
Sampling 3D Molecular Conformers with Diffusion Transformers [2025]
Frank, J. T., Ripken, W., Lied, G., MÃžller, K. R., Unke, O. T., & Chmiela, S.
arXiv:2506.15378 (2025) | code
AGDIFF: Attention-Enhanced Diffusion for Molecular Geometry Prediction [2025]
André Brasil Vieira Wyzykowski, Fatemeh Fathi Niazi, and Alex Dickson.
J. Chem. Inf. Model. (2025) |ChemRxiv. (2024) | code
Improving Structural Plausibility in 3D Molecule Generation via Property-Conditioned Training with Distorted Molecules [2024]
Lucy Vost, Vijil Chenthamarakshan, Payel Das, Charlotte M. Deane.
bioRxiv. (2024)
Diffusion-based generative AI for exploring transition states from 2D molecular graphs [2024]
Kim, S., Woo, J. & Kim, W.Y.
Nat Commun 15, 341 (2024) | code
Physics-informed generative model for drug-like molecule conformers [2024]
David C. Williams, Neil Imana.
arXiv:2403.07925. (2024) | code
DynamicsDiffusion: Generating and Rare Event Sampling of Molecular Dynamic Trajectories Using Diffusion Models [2023]
Petersen, Magnus, Gemma Roig, and Roberto Covino.
NeurIPS 2023 AI4Science (2023)
Generating Molecular Conformer Fields [2023]
Yuyang Wang, Ahmed Elhag, Navdeep Jaitly, Joshua Susskind, Miguel Bautista.
[NeurIPS 2023 Generative AI and Biology (GenBio) Workshop (2023)]https://openreview.net/forum?id=Od1KtMeAYo)
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space [2023]
Zhou, Z., Liu, R. and Yu, T.
arXiv:2310.04915 (2023))
Molecular Conformation Generation via Shifting Scores [2023]
Zhou, Zihan, Ruiying Liu, Chaolong Ying, Ruimao Zhang, and Tianshu Yu.
arXiv:2309.09985 (2023)
EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant Consistency [2023]
Fan, Zhiguang, Yuedong Yang, Mingyuan Xu, and Hongming Chen.
arXiv:2308.00237 (2023)
Torsional diffusion for molecular conformer generation [2022]
Jing, Bowen, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola.
NeurIPS. (2022) | code
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation [2022]
Xu, Minkai, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang.
International Conference on Learning Representations. (2022) | code

RL-based Molecular Conformation Generation

Conformer-RL: A deep reinforcement learning library for conformer generation [2022]
Jiang, Runxuan, Tarun Gogineni, Joshua Kammeraad, Yifei He, Ambuj Tewari, and Paul M. Zimmerman.
Journal of Computational Chemistry 43.27 (2022) | code

GNN-based Molecular Conformation Generation

Leveraging 2D Molecular Graph Pretraining for Improved 3D Conformer Generation with Graph Neural Networks [2024]
Jiang, Runxuan, Tarun Gogineni, Joshua Kammeraad, Yifei He, Ambuj Tewari, and Paul M. Zimmerman.
Computers & Chemical Engineering (2024) | code

Deep Learning-based drug design

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform [2026]
Gu, S., Zhang, X., Xiao, M. et al.
Nat Protoc (2026) | code
Generative AI-Based Drug Design: Target-Aware Molecular Generation for EGFR Type I Inhibitors with Multiplatform Docking Validation [2026]
Taqdees Khan, Young Beom Kwak, and Hee Cheol Kim.
ACS Omega (2026) | code
Bridging Generative AI and Synthesis-on-Demand Chemical Spaces for Structure-based De Novo Drug Design [2026]
Cosmin Ichim, Viet-Khoa Tran-Nguyen, Pedro J. Ballester.
ChemRxiv.(2026) | code
Generative Artificial Intelligence Optimization of Albumin Binders: Coumarin and Fatty Acid Derivatives [2026]
Yihao Zhang, Qirui Deng, Xin Yang, Xinchen Yue, Huarui Zhang, Shijian Ding, Jinping Lei, Baoting Zhang, Sifan Yu, and Ge Zhang.
J. Chem. Inf. Model.(2026) | code
Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes [2026]
Xing, J., Tan, M., Leshchiner, D., Sun, M., Abdelgied, M., Huang, L., Paithankar, S., Uhl, K., Shankar, R., Lisabeth, E. and Aleiwi, B.
Cell (2026) | code
Unified modeling of 3D molecular generation via atomic interactions with PocketXMol [2026]
Peng, X., Guo, R., Guo, F., Wang, Z., Sun, J., Guan, J., Jia, Y., Xu, Y., Huang, Y., Zhang, M. and Peng, J.
Cell (2026) | code
Comparative Evaluation of Molecular Generation Methods for Bioactive Compound Design: A Rigorous Statistical Benchmark Across Drug, Agrochemical, and Small Molecule Datasets [2025]
Mustamin SB, Hamid FH.
ChemRxiv. (2025)
ADMETrix: ADMET-Driven De Novo Molecular Generation [2025]
Mourdoukoutas N, Korfiati A, Pitsikalis V.
ChemRxiv. (2025)
Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design [2024]
Quinn, T.R., Giblin, K.A., Thomson, C., Boerth, J.A., Bommakanti, G., Braybrooke, E., Chan, C., Chinn, A.J., Code, E., Cui, C. and Fan, Y.
J. Med. Chem. (2024) | code
Reinvent 4: Modern AI–driven generative molecule design [2024]
Hannes H. Loeffler, Jiazhen He, Alessandro Tibo, Jon Paul Janet, Alexey Voronov, Lewis H. Mervin & Ola Engkvist
Journal of Cheminformatics,16(20) (2024) | code
Chemistry42: An AI-Driven Platform for Molecular Design and Optimization [2023]
Ivanenkov, Yan A., Daniil Polykovskiy, Dmitry Bezrukov, Bogdan Zagribelnyy, Vladimir Aladinskiy, Petrina Kamya, Alex Aliper, Feng Ren, and Alex Zhavoronkov.
Journal of Chemical Information and Modeling 63.3 (2023) | web

RNN-based

Generative Artificial Intelligence Optimization of Albumin Binders: Coumarin and Fatty Acid Derivatives [2026]
Yihao Zhang, Qirui Deng, Xin Yang, Xinchen Yue, Huarui Zhang, Shijian Ding, Jinping Lei, Baoting Zhang, Sifan Yu, and Ge Zhang.
J. Chem. Inf. Model.(2026) | code
A data-driven generative strategy to avoid reward hacking in multi-objective molecular design [2025]
Yoshizawa, T., Ishida, S., Sato, T. et al.
Nat Commun 16, 2409 (2025) | code
Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design [2024]
Matsukiyo, Y., Tengeiji, A., Li, C. and Yamanishi, Y.
J. Chem. Inf. Model. (2024) | code
Prospective de novo drug design with deep interactome learning [2024]
Atz, K., Cotos, L., Isert, C. et al.
Nat Commun 15, 3408 (2024) | code
CNSMolGen: a bidirectional recurrent neural networks based generative model for de novo central nervous system drug design [2024]
Gou, Rongpei, Jingyi Yang, Menghan Guo, Yingjun Chen, and Weiwei Xue.
chemrxiv-2024-x4wbl (2024) | code
NovoMol: Recurrent Neural Network for Orally Bioavailable Drug Design and Validation on PDGFRα Receptor [2023]
Rao, Ishir.
arXiv:2312.01527 (2023) | code
Generation of focused drug molecule library using recurrent neural network [2023]
Zou, Jinping, Long Zhao, and Shaoping Shi.
Journal of Molecular Modeling 29.12 (2023) | code
ChemTSv2: Functional molecular design using de novo molecule generator [2023]
Ishida, Shoichi, Tanuj Aasawat, Masato Sumita, Michio Katouda, Tatsuya Yoshizawa, Kazuki Yoshizoe, Koji Tsuda, and Kei Terayama.
Wiley Interdisciplinary Reviews: Computational Molecular Science (2023) | code
Utilizing Reinforcement Learning for de novo Drug Design [2023]
Svensson, Hampus Gummesson, Christian Tyrchan, Ola Engkvist, and Morteza Haghir Chehreghani.
arXiv:2303.17615 (2023) | code
De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning [2023]
Hu, P., Zou, J., Yu, J. et al.
J Mol Model 29, 121 (2023) | code
On The Difficulty of Validating Molecular Generative Models Realistically: A Case Study on Public and Proprietary Data [2023]
Handa, Koichi, Morgan Thomas, Michiharu Kageyama, Takeshi Iijima, and Andreas Bender.
chemrxiv-2023-lbvgn | code
Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration [2023]
Chen, Lin, Qing Shen, and Jungang Lou.
BMC Bioinformatics (2023) | code
Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation [2022]
Thomas, M., O’Boyle, N.M., Bender, A. et al.
J Cheminform (2022) | code
De novo molecule design with chemical language models [2022]
Grisoni, F., Schneider, G.
Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390.(2022) | code
Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime [2022]
Li, Chuan, Chenghui Wang, Ming Sun, Yan Zeng, Yuan Yuan, Qiaolin Gou, Guangchuan Wang, Yanzhi Guo, and Xuemei Pu.
J. Chem. Inf. Model. (2022) | code
Optimizing Recurrent Neural Network Architectures for De Novo Drug Design [2021]
Santos, B. P., Abbasi, M., Pereira, T., Ribeiro, B., & Arrais, J. P.
Paper | code
A recurrent neural network (RNN) that generates drug-like molecules for drug discovery [2021]
code
A molecule generative model used interaction fingerprint (docking pose) as constraints [2021]
code
Bidirectional Molecule Generation with Recurrent Neural Networks [2020]
Grisoni, F., Moret, M., Lingwood, R., & Schneider, G.
J. Chem. Inf. Model. (2020) | code
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks [2019]
Kotsias, PC., Arús-Pous, J., Chen, H. et al.
Nat Mach Intell 2, 254–265 (2020) | code
ChemTS: An Efficient Python Library for de novo Molecular Generation [2017]
Yang, X., Zhang, J., Yoshizoe, K., Terayama, K., & Tsuda, K.
Science and Technology of Advanced Materials (2017) | code

LSTM-based

Generative AI-Based Drug Design: Target-Aware Molecular Generation for EGFR Type I Inhibitors with Multiplatform Docking Validation [2026]
Taqdees Khan, Young Beom Kwak, and Hee Cheol Kim.
ACS Omega (2026) | code
RFL: Simplifying Chemical Structure Recognition with Ring-Free Language [2025]
Chang, Q., Chen, M., Pi, C., Hu, P., Zhang, Z., Ma, J., ... & Hu, J.
AAAI 2025 Oral. (2025) | code
Going beyond SMILES enumeration for generative deep learning in low data regimes [2025]
Brinkmann, Helena, Antoine Argante, Hugo ter Steege, and Francesca Grisoni.
ChemRxiv. (2025) | code
Stereochemistry-aware string-based molecular generation [2024]
Tom G, Yu E, Yoshikawa N, Jorner K, Aspuru-Guzik A.
ChemRxiv. (2024) | code
ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning [2024]
Wang, M., Li, S., Wang, J. et al.
Nat Commun 15, 10127 (2024) | code
DigFrag as a digital fragmentation method used for artificial intelligence-based drug design [2024]
Yang, R., Zhou, H., Wang, F. et al.
Commun Chem 7, 258 (2024) | code
Prospective de novo drug design with deep interactome learning [2024]
Atz, K., Cotos, L., Isert, C. et al.
Nat Commun 15, 3408 (2024) | code
Computational Drug Discovery on HIV Virus with a Customized LSTM Variational Autoencoder Deep Learning Architecture [2023]
Kutsal, Mucahit, Ferhat Ucar, and Nida Kati.
CPT: Pharmacometrics & Systems Pharmacology. (2023) | code
Structured State-Space Sequence Models for De Novo Drug Design [2023]
Özçelik R, de Ruiter S, Grisoni F.
chemrxiv-2023-jwmf3. (2023) | code
Integrating synthetic accessibility with AI-based generative drug design [2023]
Parrot, M., Tajmouati, H., da Silva, V.B.R. et al.
J Cheminform 15, 83 (2023) | code
Deep interactome learning for de novo drug design [2023]
Atz K, Cotos Muñoz L, Isert C, Håkansson M, Focht D, Nippa DF, et al.
chemrxiv-2023-cbq9k (2023)
Deep learning driven de novo drug design based on gastric proton pump structures [2023]
Abe, K., Ozako, M., Inukai, M. et al.
Commun Biol 6, 956 (2023) | code
Artificial Intelligence for Prediction of Biological Activities and Generation of molecular hits using Stereochemical Information [2023]
Pereira, Tiago O., Maryam Abbasi, Rita I. Oliveira, Romina A. Guedes, Jorge AR Salvador, and Joel P. Arrais.
Research Square. (2023) | code
LOGICS: Learning optimal generative distribution for designing de novo chemical structures [2023]
Bae, B., Bae, H. & Nam, H.
J Cheminform 15, 77 (2023) | code
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design [2023]
Kotsias, PC., Arús-Pous, J., Chen, H. et al.
Nat Commun 14, 114 (2023) | code
SMILES-based CharLSTM with finetuning and goal-directed generation via policy gradient [2022]

code
DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues [2022]
Creanza, T. M., Lamanna, G., Delre, P., Contino, M., Corriero, N., Saviano, M., ... & Ancona, N.
J. Chem. Inf. Model. (2022) | Web
De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning [2021]
Santana, M.V.S., Silva-Jr, F.P.
BMC Chemistry 15, 8 (2021) | code
Generative Recurrent Networks for De Novo Drug Design [2018]
Gupta, A., Müller, A. T., Huisman, B. J., Fuchs, J. A., Schneider, P., & Schneider, G.
Mol Inform. 2018 | code
Generative Recurrent Neural Networks for De Novo Drug Design [2017]
Gupta, Anvita, et al.
Mol Inform. 2018 | code

Autoregressive-models

NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer [2026]
Roxane Axel Jacob, Daniel Rose, Thierry Langer, Johannes Kirchmair.
arXiv:2609.05097 (2026) | code
Generalizing molecular design via flexible search space control [2026]
Fujii, S., Murakami, Y., Yoshizawa, T. et al.
Commun Chem (2026) | code
MolMiner: Toward Controllable, Three-Dimensional-Aware, Fragment-Based Molecular Design [2026]
Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen.
Advanced Intelligent Discovery (2026) | code
Autoregressive latent diffusion for 3D molecule generation [2026]
Federico Ottomano, Gaopeng Ren, Yingzhen Li, Kim E. Jelfs, Alex M. Ganose.
arXiv:2607.09277 (2026)
Sampling out-of-distribution chemical spaces via Bayesian flow [2026]
Tao, N., Abe, M.
J Cheminform (2026) | code
Molexar: A Unified Multimodal Molecular Foundation Model for Drug Design [2026]
Haoyu Lin, Yiyan Liao, Jinmei Pan, Xinliao Ling, Luhua Lai, Jianfeng Pei.
arXiv:2606.25865 (2026) | code
OmniInvent: Unified Generative Chemical Language Models through Blank Infilling [2026]
Nicklas Österbacka, Jon Paul Janet, Jiazhen He.
ChemRxiv.(2026)
Sample-efficient generative molecular design using memory manipulation [2026]
Guo, J., Chen, J., GX-Chen, A. et al.
Nat Mach Intell (2026) | code
NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation [2025]
Daniel Rose, Roxane Axel Jacob, Johannes Kirchmair, Thierry Langer.
arXiv:2512.05844 (2025)
MolMiner: Towards Controllable, 3D-Aware, Fragment-Based Molecular Design [2025]
Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen.
arXiv:2411.06608 (2025) | code
Scalable Autoregressive 3D Molecule Generation [2025]
Austin H. Cheng, Chong Sun, Alán Aspuru-Guzik.
arXiv:2505.13791 (2025)
Versatile Molecular Editing via Multimodal and Group-optimized Generative Learning [2024]
Lin X, Xia Y, Huang Y, Liu S, Zhang J, Gao YQ.
ChemRxiv. (2024) | code
Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation [2024]
Jeff Guo, Philippe Schwaller.
arXiv:2405.17066 (2024) | code
Enabling target-aware molecule generation to follow multi objectives with Pareto MCTS [2024]
Yang, Y., Chen, G., Li, J. et al.
Commun Biol 7, 1074 (2024) | code
PocketFlow is a data-and-knowledge-driven structure-based molecular generative model [2024]
Shengyong Yang, Yuanyuan Jiang, Guo Zhang et al.
Nat Mach Intell (2024) | Research Square. PREPRINT. (2023) | code
De Novo Molecule Design Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning [2024]
Sattari, Kianoosh, Dawei Li, Bhupalee Kalita, Yunchao Xie, Fatemeh Barmaleki Lighvan, Olexandr Isayev, and Jian Lin.
Digital Discovery (2024) | code
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation [2024]
Ameya Daigavane and Song Eun Kim and Mario Geiger and Tess Smidt.
ICLR (2024) | code
Autoregressive fragment-based for pocket-aware ligand design [2023]
Ghorbani, Mahdi, Leo Gendelev, Paul Beroza, and Michael Keiser.
NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. (2023) | code
Learning on topological surface and geometric structure for 3D molecular generation [2023]
Zhang, Odin, Tianyue Wang, Gaoqi Weng, Dejun Jiang, Ning Wang, Xiaorui Wang, Huifeng Zhao et al.
Nat Comput Sci (2023) | code
ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling [2023]
Zhang, O., Zhang, J., Jin, J. et al.
Nat Mach Intell (2023) | code
FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization [2023]
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) | code
Domain-Agnostic Molecular Generation with Self-feedback [2023]
Yin Fang, Ningyu Zhang, Zhuo Chen, Xiaohui Fan, Huajun Chen
arXiv:2301.11259v3 | code
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation [2020]
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., & Tang, J.
ICLR (2020) |arXiv:2001.09382 | code

Transformer-based

Ensemble-Conditioned Molecular Design [2026]
Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson.
arXiv:2609.15077(2026) | code
AET5: A transcriptome-guided molecular generation framework with contrastive self-supervised learning [2026]
Yuan, Zhikang, Xin Zhang, Gaoming Lin, Quan Zou, Subhashisa Swain, Yijie Ding, Prayag Tiwari, Shuofeng Yuan, and Xiaoyi Guo.
PLOS Computational Biology (2026) | code
NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer [2026]
Roxane Axel Jacob, Daniel Rose, Thierry Langer, Johannes Kirchmair.
arXiv:2609.05097 (2026) | code
RiemannMol I: Molecular Generation with Learned Latent Metrics [2026]
Xichen Zhang, Yizhou Ma, Xin Chen.
ChemRxiv. (2026) | code
Learning a chemistry-aware latent space for molecular encoding and generation with a large-scale Transformer Variational Autoencoder [2026]
Hugo Talibart, Dimitri Gilis.
J Cheminform (2026) | bioRxiv (2025) | code
Cost-effective federated molecular design with FedMol [2026]
Dong T., You L. and Chen C.
The Innovation Drug Discovery (2026)
OmniInvent: Unified Generative Chemical Language Models through Blank Infilling [2026]
Nicklas Österbacka, Jon Paul Janet, Jiazhen He.
ChemRxiv. (2026)
VeGA-RX and VeGA-SCX: Controllable SMARTS-Guided Generative Transformers for Precision-Driven De Novo Drug Design [2026]
Pietro Delre, Giada Bellofatto, and Antonio Lavecchia.
J. Chem. Inf. Model.(2026) | code
From Holo Pockets to Electron Density: GPT-style Drug Design with Density [2026]
Jiahao Chen, Letian Gao, Yanhao Zhu, Wenbiao Zhou, Bing Su, Zhi John Lu, Bo Huang.
arXiv:2605.08767 (2026) | code
ANNalog: generation of MedChem-similar molecules [2026]
Dai, W., Tyzack, J.D., Fornili, A. et al.
J Cheminform 18, 53 (2026) | code
Structure-guided molecular design with contrastive 3D protein-ligand learning [2026]
Carles Navarro, Philipp Tholke, Gianni de Fabritiis.
arXiv:2604.19562 (2026)
Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations [2026]
Pang, H.W., Zhang, P.Z., Pan, B., Zhao, L., Yu, X. and Zhang, L.
J. Chem. Inf. Model. (2026) | ChemRxiv. (2026)
Sample-efficient generative molecular design using memory manipulation [2026]
Guo, J., Chen, J., GX-Chen, A. et al.
Nat Mach Intell (2026) | code
LLMsFold: Integrating Large Language Models and Biophysical Simulations for De Novo Drug Design [2026]
Wageesha Widuranga, Fabio Bove, Dario Righelli, Salvatore Romano, Rosa Visone, Marilena V. Iorio, Pietro Lio, Cristian Taccioli.
bioRxiv (2026) | code
NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation [2025]
Daniel Rose, Roxane Axel Jacob, Johannes Kirchmair, Thierry Langer.
arXiv:2512.05844 (2025)
RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design [2025]
Xiaobo Lin, Debsindhu Bhowmik, Logan T. Kearney, and Amit K. Naskar.
J. Chem. Inf. Model. (2025) | code
Unraveling learning characteristics of transformer models for molecular design [2025]
Roth and Bajorath, .
Patterns (2025) | code
MolOrgGPT: De novo Generation via Large Language Models and Reinforcement Learning [2025]
Varas P, Toledano O, Campillo N, Ríos Insua D.
ChemRxiv. (2025)
Exploring the macrocyclic chemical space for heuristic drug design with deep learning models [2025]
Hu, F., Jia, X., Liao, W. et al.
Commun Chem 8, 299 (2025) | code
Generative AI for navigating synthesizable chemical space [2025]
W. Gao,S. Luo, & C.W. Coley.
Proc. Natl. Acad. Sci. (2025) | code
CNSGT: Generative Transformer for De Novo Drug Design Targeting the Central Nervous System [2025]
Yingjun Chen, Ding Luo, Shengneng Chen, Tingting Hou, Chao Huang, and Weiwei Xue.
J. Chem. Inf. Model. (2025) | code
TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality [2025]
Carlos Vonessen and Charles Harris and Miruna Cretu and Pietro Liò.
arXiv:2507.00899 (2025) | code
FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design [2025]
Xuefeng Liu, Songhao Jiang, Qinan Huang, Tinson Xu, Ian Foster, Mengdi Wang, Hening Lin, Jinbo Xu, Rick Stevens.
arXiv:2509.11044 (2025)
Target-specific de novo design of drug candidate molecules with graph-transformer-based generative adversarial networks [2025]
Ünlü, A., Çevrim, E., Yiğit, M.G. et al.
Nat Mach Intell (2025) | code
Leveraging tree-transformer VAE with fragment tokenization for high-performance large chemical model generation [2025]
Inukai, T., Yamato, A., Akiyama, M. et al.
Commun Chem 8, 228 (2025) | code
Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning [2025]
Salma J. Ahmed, Emad A. Mohammed.
arXiv:2405.06836 (2025)
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning [2025]
Zholus, Artem, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Daniil Polykovskiy, Sarath Chandar, and Alex Zhavoronkov.
AAAI-25 Technical Tracks (2025) | arXiv:2406.03686 (2024)
Graph Generative Pre-trained Transformer [2025]
Xiaohui Chen, Yinkai Wang, Jiaxing He, Yuanqi Du, Soha Hassoun, Xiaolin Xu, Li-Ping Liu.
arXiv:2501.01073 (2025) | code
ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization [2025]
Xuefeng Liu, Songhao Jiang, Ian Foster, Jinbo Xu, Rick Stevens.
arXiv:2502.06891 (2025) | code
Accelerating discovery of bioactive ligands with pharmacophore-informed generative models [2025]
Li Y, Lin X, Hao Y, Zhang J, Gao YQ.
ChemRxiv. (2025) | code
Accelerating discovery of bioactive ligands with pharmacophore-informed generative models [2025]
Xie, W., Zhang, J., Xie, Q. et al.
Nat Commun 16, 2391 (2025) | code
3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery [2025]
Xiuyuan Hu, Guoqing Liu, Can Chen, Yang Zhao, Hao Zhang, Xue Liu.
arXiv:2502.05107 (2025) | code
Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning [2025]
Nakamura, S., Yasuo, N. & Sekijima, M.
Commun Chem 8, 40 (2025) | code
Expanding Chemical Space: Developing a Compound Generative Pre-trained Transformer for De Novo Drug Design [2025]
Dai Z, Zhang J, Zhong S, Fu J, Deng Y, Zhang D, et al.
bioRxiv. (2025) | code
A Zero-Shot Single-point Molecule Optimization Model: Mimicking Medicinal Chemists’ Expertise [2025]
Dai Z, Zhang J, Zhong S, Fu J, Deng Y, Zhang D, et al.
chemrxiv-2025-m82r5 (2025) | code
Molecular Generation with State Space Sequence Models [2024]
Anri Lombard, Shane Acton, Ulrich Armel Mbou Sob, Jan Buys.
NeurIPS 2024 Workshop on AI for New Drug Modalities (2024) | code
3DSMILES-GPT: 3D Molecular Pocket-based Generation with Token-only Large Language Model [2024]
Wang, Jike, Hao Luo, Rui Qin, Mingyang Wang, Meijing Fang, Odin Zhang, Qiaolin Gou et al.
Chemical Science (2024) | code
GP-MoLFormer: A Foundation Model For Molecular Generation [2024]
Ross, Jerret, Brian Belgodere, Samuel C. Hoffman, Vijil Chenthamarakshan, Jiri Navratil, Youssef Mroueh, and Payel Das.
arXiv:2405.04912 (2024) | code
Generative Artificial Intelligence for Navigating Synthesizable Chemical Space [2024]
Wenhao Gao, Shitong Luo, Connor W. Coley.
arXiv:2410.03494 (2024) | code
Diffusion-based generative drug-like molecular editing with chemical natural language [2024]
Jianmin Wang, Peng Zhou, Zixu Wang, Wei Long, Yangyang Chen, Kyoung Tai No, Dongsheng Ouyang*，Jiashun Mao* and Xiangxiang Zeng*.
J. Pharm. Anal. (2024) | code
Leveraging Tree-Transformer VAE with fragment tokenization for high-performance large chemical generative model [2024]
Inukai T, Yamato A, Akiyama M, Sakakibara Y.
ChemRxiv. (2024) | code
A deep learning approach for rational ligand generation with toxicity control via reactive building blocks [2024]
Li, P., Zhang, K., Liu, T. et al.
Nat Comput Sci (2024) | code
A Foundation Model for Chemical Design and Property Prediction [2024]
Cai, F., Zhu, T., Tzeng, T.R., Duan, Y., Liu, L., Pilla, S., Li, G. and Luo, F.
arXiv:2410.21422 (2024) | code
SE(3) Equivariant Topologies for Structure-based Drug Discovery [2024]
Prat A, Abdel Aty H, Pabrinkis A, Bastas O, Paquet T, Kamuntavičius G, et al.
ChemRxiv. (2024)
Graph Diffusion Transformers for Multi-Conditional Molecular Generation [2024]
Liu, Gang, Jiaxin Xu, Te Luo and Meng Jiang.
NeurIPS 2024 (Oral). (2024) | code
Exhaustive local chemical space exploration using a transformer model [2024]
Tibo, A., He, J., Janet, J.P. et al.
Nat Commun 15, 7315 (2024) | code
Transformer Graph Variational Autoencoder for Generative Molecular Design [2024]
Nguyen, Trieu, and Aleksandra Karolak.
bioRxiv (2024)
Exploring Novel Fentanyl Analogues Using a Graph-Based Transformer Model [2024]
Zhang, Guangle, Yuan Zhang, Ling Li, Jiaying Zhou, Honglin Chen, Jinwen Ji, Yanru Li, Yue Cao, Zhihui Xu, and Cong Pian.
Interdisciplinary Sciences: Computational Life Sciences (2024) | code
TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation [2024]
Li, Chen, and Yoshihiro Yamanishi.
International Conference on Artificial Intelligence and Statistics. PMLR (2024)
DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation [2024]
Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, Xun Wang.
Briefings in Functional Genomics (2024) | code
Gotta be SAFE: A New Framework for Molecular Design [2024]
Noutahi, Emmanuel, Cristian Gabellini, Michael Craig, Jonathan SC Lim, and Prudencio Tossou.
Digital Discovery (2024) | arXiv:2310.10773 (2023) | code
Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms [2024]
Bhowmik, Debsindhu, Pei Zhang, Zachary Fox, Stephan Irle, and John Gounley.
Patterns (2024) | code
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation [2024]
Kyro, Gregory W., Anton Morgunov, Rafael I. Brent, and Victor S. Batista.
J. Chem. Inf. Model. (2024) | code
Evaluation of Reinforcement Learning in Transformer-based Molecular Design [2024]
He J, Tibo A, Janet JP, Nittinger E, Tyrchan C, Czechtizky W, et al.
chemrxiv-2024-r9ljm (2024) | code
Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer [2024]
Deqian Kong and Yuhao Huang and Jianwen Xie and Edouardo Honig and Ming Xu and Shuanghong Xue and Pei Lin and Sanping Zhou and Sheng Zhong and Nanning Zheng and Ying Nian Wu.
arXiv:2402.17179 (2024)
A novel molecule generative model of VAE combined with Transformer [2024]
Yasuhiro Yoshikai and Tadahaya Mizuno and Shumpei Nemoto and Hiroyuki Kusuhara.
arXiv:2402.11950 (2024) | code
GexMolGen: Cross-modal Generation of Hit-like Molecules via Large Language Model Encoding of Gene Expression Signatures [2024]
Cheng, Jia-Bei, Xiaoyong Pan, Kaiyuan Yang, Shenghao Cao, Bin Liu, Qingran Yan, and Ye Yuan.
bioRxiv (2024) | code
Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors [2024]
Weichen Bo, Yangqin Duan, Yurong Zou, Ziyan Ma, Tao Yang, Peng Wang, Tao Guo, Zhiyuan Fu, Jianmin Wang, Linchuan Fan, Jie liu, Taijin Wang, and Lijuan Chen.
J. Chem. Inf. Model. (2024) | code
Target-aware Molecule Generation for Drug Design Using a Chemical Language Model [2024]
Xia, Yingce, Kehan Wu, Pan Deng, Renhe Liu, Yuan Zhang, Han Guo, Yumeng Cui et al.
bioRxiv (2024)
Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models [2024]
Xie, Weixin, Jianhang Zhang, Qin Xie, Chaojun Gong, Youjun Xu, Luhua Lai, and Jianfeng Pei.
arXiv:2401.01059 (2024) | code
A self-improvable Polymer Discovery Framework Based on Conditional Generative Model [2023]
Xiangyun Lei and Weike Ye and Zhenze Yang and Daniel Schweigert and Ha-Kyung Kwon and Arash Khajeh.
arXiv:2312.04013. (2023)
LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design [2023]
Dobberstein, Niklas, Astrid Maass, and Jan Hamaekers.
arXiv:2311.14407. (2023) | code
GraphGPT: A Graph Enhanced Generative Pretrained Transformer for Conditioned Molecular Generation [2023]
Lu, Hao, Zhiqiang Wei, Xuze Wang, Kun Zhang, and Hao Liu.
International Journal of Molecular Sciences 24.23 (2023) | code
PROTACable is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning to Automate the De Novo Design of PROTACs [2023]
Hazem Mslati, Francesco Gentile, Mohit Pandey, Fuqiang Ban, Artem Cherkasov.
bioRxiv 2023.11.20.567951. (2023) | code
Molecular generation strategy and optimization based on A2C reinforcement learning in de novo drug design [2023]
Wang, Qian, Zhiqiang Wei, Xiaotong Hu, Zhuoya Wang, Yujie Dong, and Hao Liu.
Bioinformatics: btad693. (2023) | code
Cross-modal Generation of Hit-like Molecules via Foundation Model Encoding of Gene Expression Signatures [2023]
Jiabei Cheng, Xiaoyong Pan, Kaiyuan Yang, Shenghao Cao, Bin Liu, Ye Yuan.
bioRxiv 2023.11.11.566725. (2023) | code
REINVENT4: Modern AI–Driven Generative Molecule Design [2023]
Loeffler H, He J, Tibo A, Janet JP, Voronov A, Mervin L, et al.
chemrxiv-2023-xt65x (2023) | code
Optimization of binding affinities in chemical space with transformer and deep reinforcement learning [2023]
Xu, Xiaopeng, Juexiao Zhou, Chen Zhu, Qing Zhan, Zhongxiao Li, Ruochi Zhang, Yu Wang, Xingyu Liao, and Xin Gao.
chemrxiv-2023-7v4sw (2023) | code
Searching for High-Value Molecules Using Reinforcement Learning and Transformers [2023]
Raj Ghugare and Santiago Miret and Adriana Hugessen and Mariano Phielipp and Glen Berseth.
arXiv:2310.02902 (2023)
Molecular De Novo Design through Transformer-based Reinforcement Learning [2023]
Feng, Tao, Pengcheng Xu, Tianfan Fu, Siddhartha Laghuvarapu, and Jimeng Sun.
arXiv:2310.05365 (2023)
Probabilistic generative transformer language models for generative design of molecules [2023]
Wei, L., Fu, N., Song, Y. et al.
J Cheminform 15, 88 (2023) | code
De Novo Drug Design with Joint Transformers [2023]
Adam Izdebski and Ewelina Węglarz-Tomczak and Ewa Szczurek and Jakub M. Tomczak
arXiv:2310.02066. (2023)
Structured State-Space Sequence Models for De Novo Drug Design [2023]
Özçelik R, de Ruiter S, Grisoni F.
chemrxiv-2023-jwmf3. (2023) | code
De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization [2023]
Yuki Matsukiyo, Chikashige Yamanaka, and Yoshihiro Yamanishi.
J. Chem. Inf. Model. (2023) | code
A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization. [2023]
Li, C., Yamanishi, Y.
ECML PKDD (2023) | code
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation [2023]
Kyro, Gregory W., Anton Morgunov, Rafael I. Brent, and Victor S. Batista.
arXiv:2309.05853 (2023) | code
Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE [2023]
Dollar, Orion, Nisarg Joshi, Jim Pfaendtner, and David AC Beck.
The Journal of Physical Chemistry A (2023) | code
Lingo3DMol: Generation of a Pocket-based 3D Molecule using a Language Model [2023]
Wang, Lvwei, Zaiyun Lin, Yanhao Zhu, Rong Bai, Wei Feng, Huting Wang, Jielong Zhou, Wei Peng, Bo Huang, and Wenbiao Zhou.
arXiv:2305.10133 (2023) | code
FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers [2023]
Monteiro, Nelson RC, Tiago O. Pereira, Ana Catarina D. Machado, José L. Oliveira, Maryam Abbasi, and Joel P. Arrais.
Computers in Biology and Medicine (2023) | code
Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery [2023]
Diao, Y., Liu, D., Ge, H. et al.
Nat Commun 14, 4552 (2023) | code
De novo drug design based on patient gene expression profiles via deep learning [2023]
Yamanaka, Chikashige, Shunya Uki, Kazuma Kaitoh, Michio Iwata, and Yoshihiro Yamanishi.
Molecular Informatics (2023) | code
Transformer-based deep learning method for optimizing ADMET properties of lead compounds [2023]
Yang, Lijuan, Chao Jin, Guanghui Yang, Zhitong Bing, Liang Huang, Yuzhen Niu, and Lei Yang.
Physical Chemistry Chemical Physics 25.3 (2023)
Sequence-based drug design as a concept in computational drug design [2023]
Chen, L., Fan, Z., Chang, J. et al.
Nat Commun 14, 4217 (2023) | code
DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins [2023]
Yuesen Li, Chengyi Gao, Xin Song, Xiangyu Wang, View ORCID ProfileYungang Xu, Suxia Han
bioRxiv (2023) | code
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding [2023]
Gao, Zhangyang, Yuqi Hu, Cheng Tan, and Stan Z. Li.
arXiv:2302.07120 (2023) | code
Adaptive language model training for molecular designs [2023]
Andrew E. Blanchard, Debsindhu Bhowmik, Zachary Fox, John Gounley, Jens Glaser, Belinda S. Akpa & Stephan Irle.
J Cheminform 15, 59 (2023) | code
CMGN: a conditional molecular generation net to design target-specific molecules with desired properties [2023]
Yang, Minjian, Hanyu Sun, Xue Liu, Xi Xue, Yafeng Deng, and Xiaojian Wang.
Briefings in Bioinformatics, 2023;, bbad185 | code
cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation [2023]
Wang, Ye, Honggang Zhao, Simone Sciabola, and Wenlu Wang.
Molecules 2023, 28(11), 4430 | code
Molecule generation using transformers and policy gradient reinforcement learning [2023]
Mazuz, E., Shtar, G., Shapira, B. et al.
Sci Rep 13, 8799 (2023) | code
iupacGPT: IUPAC-based large-scale molecular pre-trained model for property prediction and molecule generation [2023]
Jiashun Mao,, Jianmin Wang, Kwang-Hwi Cho, Kyoung Tai No
chemrxiv-2023-5kjvh | code
Molecular Generation with Reduced Labeling through Constraint Architecture [2023]
Wang, Jike, Yundian Zeng, Huiyong Sun, Junmei Wang, Xiaorui Wang, Ruofan Jin, Mingyang Wang et al.
J. Chem. Inf. Model. (2023) | code
Generative Discovery of Novel Chemical Designs using Diffusion Modeling and Transformer Deep Neural Networks with Application to Deep Eutectic Solvents [2023]
Luu, Rachel K., Marcin Wysokowski, and Markus J. Buehler.
arXiv:2304.12400v1 | code
Regression Transformer enables concurrent sequence regression and generation for molecular language modelling [2023]
Born, J., Manica, M.
Nat Mach Intell 5, 432–444 (2023) | code
Transformer-based molecular generative model for antiviral drug design [2023]
mao, jiashun; wang, jianming; zeb, amir; Cho, Kwang-Hwi; jin, haiyan; Kim, Jongwan; Lee, Onju; Wang, Yunyun; No, Kyoung Tai.
J. Chem. Inf. Model. (2023) | code
Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks [2023]
Ünlü, Atabey, Elif Çevrim, Ahmet Sarıgün, Hayriye Çelikbilek, Heval Ataş Güvenilir, Altay Koyaş, Deniz Cansen Kahraman, Ahmet Rifaioğlu, and Abdurrahman Olğaç.
arXiv:2302.07868v5
DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning [2023]
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J Cheminform 15, 24 (2023) | code
Explore drug-like space with deep generative models [2023]
Wang, Jianmin, et al.
Methods (2023) | code
Large-scale chemical language representations capture molecular structure and properties [2022]
Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., & Das, P.
Nat Mach Intell 4, 1256–1264 (2022) | code
AlphaDrug: protein target specific de novo molecular generation [2022]
Qian, Hao, Cheng Lin, Dengwei Zhao, Shikui Tu, and Lei Xu.
PNAS Nexus (2022) | code
Can We Quickly Learn to “Translate” Bioactive Molecules with Transformer Models? [2022]
Bagal, V., Aggarwal, R., Vinod, P. K., & Priyakumar, U. D.
chemrxiv-2022-gln27
MolGPT: Molecular Generation Using a Transformer-Decoder Model [2022]
Bagal, V., Aggarwal, R., Vinod, P. K., & Priyakumar, U. D.
J. Chem. Inf. Model. 2022, 62, 9, 2064–2076 | code
Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design [2022]
Wu, K., Xia, Y., Fan, Y., Deng, P., Liu, H., Wu, L., ... & Liu, T. Y.
arXiv.2209.06158 | code
Exploiting pretrained biochemical language models for targeted drug design [2022]
Uludoğan, Gökçe, Elif Ozkirimli, Kutlu O. Ulgen, Nilgün Karalı, and Arzucan Özgür.
Bioinformatics (2022) | code
A Transformer-based Generative Model for De Novo Molecular Design [2022]
Wang, Wenlu, et al.
arXiv:2210.08749v2
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Edwards, C., Lai, T., Ros, K., Honke, G., & Ji, H.
arXiv:2204.11817v3 | code
Regression Transformer enables concurrent sequence regression and generation for molecular language modeling [2022]
Born, Jannis and Manica, Matteo
arXiv:2202.01338v3 | code
Generative Pre-Training from Molecules [2021]
Adilov, Sanjar.
J. Chem. Inf. Model. 2022, 62, 9, 2064–2076 | code
Transformers for Molecular Graph Generation [2021]
Cofala, Tim, and Oliver Kramer.
ESANN 2021 | code
Spatial Generation of Molecules with Transformers [2021]
Cofala, Tim, and Oliver Kramer.
IJCNN52387.2021.9533439 (2021) | code
Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attentio [2021]
Hyunseung Kim, Jonggeol Na*, and Won Bo Lee*.
J. Chem. Inf. Model. 2021, 61, 12, 5804–5814 | code
C5T5: Controllable Generation of Organic Molecules with Transformer [2021]
Rothchild, D., Tamkin, A., Yu, J., Misra, U., & Gonzalez, J.
arXiv:2108.10307v1 | code
Molecular optimization by capturing chemist’s intuition using deep neural networks [2021]
He, J., You, H., Sandström, E. et al.
J Cheminform 13, 26 (2021) | code
Transformer neural network for protein-specific de novo drug generation as a machine translation problem [2021]
Grechishnikova, Daria.
Sci Rep 11, 321 (2021) | code
Transmol: repurposing a language model for molecular generation [2021]
Grechishnikova, Daria.
RSC advances. 2021;11(42):25921-32. | code
Attention-based generative models for de novo molecular design [2021]
Dollar, O., Joshi, N., Beck, D.A. and Pfaendtner, J.,
Chemical Science 12.24 (2021) | code

VAE-based

Learning a chemistry-aware latent space for molecular encoding and generation with a large-scale Transformer Variational Autoencoder [2026]
Hugo Talibart, Dimitri Gilis.
J Cheminform (2026) | bioRxiv (2025) | code
QSAR-Guided Generative Framework for the Discovery of Synthetically Viable Odorants [2025]
Tim C. Pearce, Ahmed Ibrahim.
arXiv:2512.23080 (2025)
Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design [2025]
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson.
arXiv:2512.17659 (2025)
SpecLig: Energy-Guided Hierarchical Model for Target-Specific 3D Ligand Design [2025]
Peidong Zhang, Rong Han, Xiangzhe Kong, Ting Chen, Jianzhu Ma.
bioRxiv. (2025) | code
PCF-VAE: posterior collapse free variational autoencoder for de novo drug design [2025]
Bhadwal, A.S., Kumari, M. & Kumar, A.
Sci Rep 15, 34152 (2025) | code
CNSGT: Generative Transformer for De Novo Drug Design Targeting the Central Nervous System [2025]
Yingjun Chen, Ding Luo, Shengneng Chen, Tingting Hou, Chao Huang, and Weiwei Xue.
J. Chem. Inf. Model. (2025) | code
Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol [2025]
Li, M., Song, K., He, J. et al.
Nat Mach Intell 7, 1355–1368 (2025) | code
A generative deep learning approach to de novoantibiotic design [2025]
Aarti Krishnan, Melis N. Anahtar, Jacqueline A. Valeri, ..., Connor W. Coley, Felix Wong, James J. Collins.
Cell (2025) | code
Optimizing drug design by merging generative AI with a physics-based active learning framework [2025]
Filella-Merce, I., Molina, A., Díaz, L. et al.
Commun Chem 8, 238 (2025) | code
Leveraging tree-transformer VAE with fragment tokenization for high-performance large chemical model generation [2025]
Inukai, T., Yamato, A., Akiyama, M. et al.
Commun Chem 8, 228 (2025) | code
Generative molecule evolution using 3D pharmacophore for efficient Structure-Based Drug Design [2025]
Yi He, Ailun Wang, Zhi Wang, Yu Liu, Xingyuan Xu, Wen Yan.
arXiv:2507.20130 (2025)
A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules [2025]
Kim, H., Bae, B., Park, M. et al.
Nat Commun 16, 5628 (2025) | code
Multi-objective drug design with a scaffold-aware variational autoencoder [2025]
Dong, Tiejun, Linlin You, and Calvin Yu-Chian Chen.
Chem. Sci. (2025) | code
Expanding Molecular Design with Graph Variational Autoencoders: A Comparative Study of Pair-Encoding and Character Tokenization [2025]
Nguyen T, Karolak A.
ChemRxiv. (2025) | code
MolGene-E: Inverse Molecular Design to Modulate Single Cell Transcriptomics [2025]
Rahul Ohlan, Raswanth Murugan, Li Xie, Mohammedsadeq Mottaqi, Shuo Zhang, Lei Xie.
bioRxiv. (2025) | code
Leveraging Tree-Transformer VAE with fragment tokenization for high-performance large chemical generative model [2024]
Inukai T, Yamato A, Akiyama M, Sakakibara Y.
ChemRxiv. (2024) | code
Transformer Graph Variational Autoencoder for Generative Molecular Design [2024]
Nguyen, Trieu, and Aleksandra Karolak.
bioRxiv (2024)
Structure-Based Drug Design with a Deep Hierarchical Generative Model [2024]
Weller, Jesse A., and Remo Rohs.
J. Chem. Inf. Model. (2024) | code
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design [2024]
Abeer, A. N. M., Sanket Jantre, Nathan M. Urban, and Byung-Jun Yoon.
arXiv:2405.00202 (2024)
GxVAEs: Two Joint VAEs Generate Hit Molecules from Gene Expression Profiles [2024]
Li, Chen, and Yoshihiro Yamanishi.
Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 12. (2024) | code
3D molecular generative framework for interaction-guided drug design [2024]
Zhung, W., Kim, H. & Kim, W.Y.
Nat Commun 15, 2688 (2024) | code
Attention Based Molecule Generation via Hierarchical Variational Autoencoder [2024]
Divahar Sivanesan.
arXiv:2402.16854. (2024)
Multi-objective latent space optimization of generative molecular design models [2024]
Abeer, A N M Nafiz et al.
Patterns 5.10 (2024) | code
A novel molecule generative model of VAE combined with Transformer [2024]
Yasuhiro Yoshikai and Tadahaya Mizuno and Shumpei Nemoto and Hiroyuki Kusuhara.
arXiv:2402.11950 (2024) | code
De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning [2024]
He, Dakuo, Qing Liu, Yan Mi, Qingqi Meng, Libin Xu, Chunyu Hou, Jinpeng Wang et al.
Advanced Science (2024)
Computational Drug Discovery on HIV Virus with a Customized LSTM Variational Autoencoder Deep Learning Architecture [2023]
Kutsal, Mucahit, Ferhat Ucar, and Nida Kati.
CPT: Pharmacometrics & Systems Pharmacology. (2023) | code
NRC-VABS: Normalized Reparameterized Conditional Variational Autoencoder with applied beam search in latent space for drug molecule design [2023]
Bhadwal, Arun Singh, Kamal Kumar, and Neeraj Kumar.
Expert Systems with Applications. (2023)
Target-Aware Variational Auto-Encoders for Ligand Generation with Multi-Modal Protein Modeling [2023]
Ngo, Khang, and Truong Son Hy.
NeurIPS 2023 Generative AI and Biology (GenBio) Workshop. (2023) | code
Interaction-aware 3D Molecular Generative Framework for Generalizable Structure-based Drug Design [2023]
Woo Youn Kim, Wonho Zhung, and Hyeongwoo Kim.
Research Square. (2023) | code
Application of variational graph encoders as an effective generalist algorithm in computer-aided drug design [2023]
Lam, H.Y.I., Pincket, R., Han, H. et al.
Nat Mach Intell 5, 754–764 (2023) | code
De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization [2023]
Yuki Matsukiyo, Chikashige Yamanaka, and Yoshihiro Yamanishi.
J. Chem. Inf. Model. (2023) | code
ReBADD-SE: Multi-objective molecular optimisation using SELFIES fragment and off-policy self-critical sequence training [2023]
Choi, Jonghwan, Sangmin Seo, Seungyeon Choi, Shengmin Piao, Chihyun Park, Sung Jin Ryu, Byung Ju Kim, and Sanghyun Park.
Computers in Biology and Medicine 157 (2023) | code
Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE [2023]
Dollar, Orion, Nisarg Joshi, Jim Pfaendtner, and David AC Beck.
The Journal of Physical Chemistry A (2023) | code
Multi-constraint molecular generation using sparsely labelled training data for localized high-concentration electrolyte diluent screening [2023]
Mailoa, Jonathan P., Xin Li, Jiezhong Qiu, and Shengyu Zhang.
Digital Discovery (2023) | code | Dataset
Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex [2023]
Feng, Hongsong, Rui Wang, Chang-Guo Zhan, and Guo-Wei Wei.
J. Med. Chem. (2023) | code
ScaffoldGVAE: Scaffold Generation and Hopping of Drug Molecules via a Variational Autoencoder Based on Multi-View Graph Neural Networks [2023]
Hu, Chao, Song Li, Chenxing Yang, Jun Chen, Yi Xiong, Guisheng Fan, Hao Liu, and Liang Hong.
J Cheminform 15, 91 (2023) | Research Square. (2023) | code
Deep Generative Design of Porous Organic Cages via a Variational Autoencoder [2023]
Jiajun Zhou, Austin Mroz, Kim Jelfs*.
chemrxiv (2023) | code
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning [2023]
Nhat Khang Ngo, Truong Son Hy.
bioRxiv. (2023) | code
De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework [2023]
Salas-Estrada, Leslie, Davide Provasi, Xing Qiu, H. Umit Kaniskan, Xi-Ping Huang, Jeffrey DiBerto, Joao Marcelo Lamim Ribeiro, Jian Jin, Bryan L. Roth, and Marta Filizola.
J. Chem. Inf. Model. (2023) | code
Deep generative model of constructing chemical latent space for large molecular structures with 3D complexity [2023]
Ochiai, Toshiki, Tensei Inukai, Manato Akiyama, Kairi Furui, Masahito Ohue, Nobuaki Matsumori, Shinsuke Inuki et al.
Commun Chem 6, 249 (2023) | chemrxiv (2023) | code
De novo drug design based on patient gene expression profiles via deep learning [2023]
Yamanaka, Chikashige, Shunya Uki, Kazuma Kaitoh, Michio Iwata, and Yoshihiro Yamanishi.
Molecular Informatics (2023) | code
Construction of order-independent molecular fragments space with vector quantised graph autoencoder [2023]
Akhmetshin, Timur and Lin, Albert and Madzhidov, Timur and Varnek, Alexandre
chemrxiv-2023-5zmvw | code
De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep Learning Framework [2023]
Salas-Estrada, Leslie, Davide Provasi, Xing Qiu, H. Umit Kaniskan, Xi-Ping Huang, Jeffrey DiBerto, Joao Marcelo Lamim Ribeiro, Jian Jin, Bryan L. Roth, and Marta Filizola.
bioRxiv (2023) | code
De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder [2023]
Salas-Estrada, Leslie, Davide Provasi, Xing Qiu, H. Umit Kaniskan, Xi-Ping Huang, Jeffrey DiBerto, Joao Marcelo Lamim Ribeiro, Jian Jin, Bryan L. Roth, and Marta Filizola.
bioRxiv (2023) | code
De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder [2023]
Pravalphruekul, Nutaya, Maytus Piriyajitakonkij, Phond Phunchongharn, and Supanida Piyayotai.
J. Chem. Inf. Model. (2023) | code
Chemical Design with GPU-based Ising Machine [2023]
Mao, Zetian, Yoshiki Matsuda, Ryo Tamura, and Koji Tsuda.
Digital Discovery (2023) | code
Accelerating drug target inhibitor discovery with a deep generative foundation model [2023]
Vijil Chenthamarakshan et al.
Sci. Adv.9,eadg7865(2023) | code
De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder [2023]
Nutaya Pravalphruekul, Maytus Piriyajitakonkij, Phond Phunchongharn, and Supanida Piyayotai.
J. Chem. Inf. Model. 2023 | code
A Protein-Ligand Interaction-focused 3D Molecular Generative Framework for Generalizable Structure-based Drug Design [2023]
Zhung W, Kim H, Kim WY.
chemrxiv-2023-jsjwx | code
VGAE-MCTS: a New Molecular Generative Model combining Variational Graph Auto-Encoder and Tree Search [2023]
Iwata, Hiroaki, Taichi Nakai, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, and Yasushi Okuno.
J. Chem. Inf. Model. (2023) | chemrxiv-2023-q8419-v2 | code
Deep Generation Model Guided by the Docking Score for Active Molecular Design [2023]
Yang, Yuwei, Chang-Yu Hsieh, Yu Kang, Tingjun Hou, Huanxiang Liu, and Xiaojun Yao.
J. Chem. Inf. Model. 2023, 63, 10, 2983–2991 | code
Direct De Novo Molecule Generation Using Probabilistic Diverse Variational Autoencoder [2023]
Singh Bhadwal, Arun, and Kamal Kumar.
Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer (2023)
MoVAE: A Variational AutoEncoder for Molecular Graph Generation [2023]
Lin, Zerun, Yuhan Zhang, Lixin Duan, Le Ou-Yang, and Peilin Zhao.
Society for Industrial and Applied Mathematics, 2023.
Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures [2023]
Das, Dibyajyoti, Broto Chakrabarty, Rajgopal Srinivasan, and Arijit Roy.
J. Chem. Inf. Model. (2023)
COMA: efficient structure-constrained molecular generation using contractive and margin losses [2023]
Choi, J., Seo, S. & Park, S.
J Cheminform 15, 8 (2023) | code
Design of potent antimalarials with generative chemistry [2022]
Godinez, W.J., Ma, E.J., Chao, A.T. et al.
Nat Mach Intell 4, 180–186 (2022) | code
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders [2022]
Stanton, S., Maddox, W., Gruver, N., Maffettone, P., Delaney, E., Greenside, P., & Wilson, A. G. PMLR 162:20459-20478, 2022
Conditional β-VAE for De Novo Molecular Generation [2022]
Richards, Ryan J., and Austen M. Groener.
arXiv:2205.01592v1
MGCVAE: Multi-Objective Inverse Design via Molecular Graph Conditional Variational Autoencoder [2022]
Lee, Myeonghun, and Kyoungmin Min.
J. Chem. Inf. Model. 2022, 62, 12, 2943–2950 | code
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design [2022]
Wang, M., Hsieh, C.Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D. and Hou, T.,
J. Med. Chem. 2022, 65, 13, 9478–9492 | code
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design [2022]
Huang, Yinan, Xingang Peng, Jianzhu Ma, and Muhan Zhang.
[Paper]https://arxiv.org/abs/2205.07309) | code
Molecule Generation by Principal Subgraph Mining and Assembling [2022]
Kong, X., Huang, W., Tan, Z., & Liu, Y.
NeurIPS 2022 |arXiv:2106.15098v4 | code
LIMO: Latent Inceptionism for Targeted Molecule Generation [2022]
Eckmann, Peter, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, and Rose Yu.
ICML (2022) | arXiv:2206.09010v1 | code
Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder [2022]
Kim, H., Ko, S., Kim, B.J. et al.
J Cheminform 14, 83 (2022) | code
Geometry-Based Molecular Generation With Deep Constrained Variational Autoencoder [2022]
Li, Chunyan, Junfeng Yao, Wei Wei, Zhangming Niu, Xiangxiang Zeng, Jin Li, and Jianmin Wang.
IEEE Transactions on Neural Networks and Learning Systems (2022) | code
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning [2021] Grosnit, A., Tutunov, R., Maraval, A.M., Griffiths, R-R., Cowen-Rivers, A.I. et al. arXiv:2106.03609v3 | code
Inverse design of nanoporous crystalline reticular materials with deep generative models. [2021]
Yao, Z., Sánchez-Lengeling, B., Bobbitt, N.S. et al.
Nat Mach Intell 3, 76–86 (2021) | code
Attention-based generative models for de novo molecular design [2021]
Dollar, O., Joshi, N., Beck, D.A. and Pfaendtner, J.,
Chemical Science 12.24 (2021) | code
Toward efficient generation, correction, and properties control of unique drug-like structures [2021]
Druchok, Maksym, Dzvenymyra Yarish, Oleksandr Gurbych, and Mykola Maksymenko.
Journal of Computational Chemistry 42.11 (2021) | code
Compressed graph representation for scalable molecular graph generation [2020]
Kwon, Youngchun, Dongseon Lee, Youn-Suk Choi, Kyoham Shin, and Seokho Kang.
J Cheminform 12, 58 (2020) | code
Deep learning enables rapid identification of potent DDR1 kinase inhibitors [2019]
Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A. et al.
Nat Biotechnol 37, 1038–1040 (2019) | code
Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data [2019]
Armitage, John, Leszek J. Spalek, Malgorzata Nguyen, Mark Nikolka, Ian E. Jacobs, Lorena Marañón, Iyad Nasrallah et al.
arXiv:1910.13325v2 | code
Molecular generative model based on conditional variational autoencoder for de novo molecular design [2018]
Lim, J., Ryu, S., Kim, J. W., & Kim, W. Y.
J Cheminform 10, 31 (2018) | code
Constrained Bayesian optimization for automatic chemical design using variational autoencoders [2017]
Griffiths, R-R., Hernández-Lobato, J. M.
Chemical Science 11, 2 (2020) | arXiv:1709.05501v6 | code
Automatic chemical design using a data-driven continuous representation of molecules [2017]
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., ... & Aspuru-Guzik, A.
ACS Cent. Sci. 2018 | arXiv:1610.02415v3 | code

GAN-based

Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design [2025]
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson.
arXiv:2512.17659 (2025)
Novel molecule design with POWGAN, a policy-optimized Wasserstein generative adversarial network [2025]
Macedo, B., Vaz, I.R. & Gomes, T.T.
J Cheminform (2025) | code
Target-specific de novo design of drug candidate molecules with graph-transformer-based generative adversarial networks [2025]
Ünlü, A., Çevrim, E., Yiğit, M.G. et al.
Nat Mach Intell (2025) | code
Interface-aware molecular generative framework for protein-protein interaction modulators [2024]
Jianmin Wang, Jiashun Mao, Chunyan Li, Hongxin Xiang, Xun Wang, Shuang Wang, Zixu Wang, Yangyang Chen, Yuquan Li, Heqi Sun, Kyoung Tai No, Tao Song, Xiangxiang Zeng
J. Cheminform. (2024) | bioRxiv (2023) | code
TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation [2024]
Li, Chen, and Yoshihiro Yamanishi.
International Conference on Artificial Intelligence and Statistics. PMLR (2024)
Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms [2024]
Bhowmik, Debsindhu, Pei Zhang, Zachary Fox, Stephan Irle, and John Gounley.
Patterns (2024) | code
Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models [2024]
Zhiwen Zhu, Jiayi Lu, Shaoxuan Yuan, Yu He, Fengru Zheng, Hao Jiang, Yuyi Yan, Qiang Sun.
J. Phys. Chem. Lett. (2024)
De Novo Molecule Design Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning [2024]
Sattari, Kianoosh, Dawei Li, Bhupalee Kalita, Yunchao Xie, Fatemeh Barmaleki Lighvan, Olexandr Isayev, and Jian Lin.
Digital Discovery (2024) | code
STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks [2023]
Zou, Jinping, Jialin Yu, Pengwei Hu, Long Zhao, and Shaoping Shi.
Computers in Biology and Medicine (2023) | code
A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization. [2023]
Li, C., Yamanishi, Y.
ECML PKDD (2023) | code
Mol-Zero-GAN: Zero-Shot Adaptation of Molecular Generative Adversarial Network for Specific Protein Targets [2023]
Ravipas Aphikulvanich*， Natapol Pornputtapong， Duangdao Wichadakul
Paper | code
De Novo Design of Molecules Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning [2023]
Sattari, Kianoosh, Dawei Li, Yunchao Xie, Olexandr Isayev, and Jian Lin.
Paper | code
MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules [2023]
Liu, X., Zhang, W., Tong, X. et al.
J Cheminform 15, 42 (2023) | code
Deep generative model for drug design from protein target sequence [2023]
Yangyang Chen, Zixu Wang, Lei Wang, Jianmin Wang, Pengyong Li, Dongsheng Cao, Xiangxiang Zeng, Xiucai Ye & Tetsuya Sakurai.
J Cheminform 15, 38 (2023) | code
Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features [2022]
Zapata, Paula A. Marin, Oscar Méndez-Lucio, Tuan Le, Carsten Jörn Beese, Jörg Wichard, David Rouquié, and Djork-Arné Clevert.
Digital Discovery (2023) | [code

papers-for-molecular-design-using-DL