related to Generative AI and Deep Learning for molecular/drug design.
[Ref: Generative Models as an Emerging Paradigm in the Chemical Sciences]
Updating ...
Molecular Optimization will welcome !!!
Datasets | Benchmarks | Drug-likeness | Evaluation metrics |
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Datasets | Benchmarks | QED | SAscore |
QEPPI | RAscore | ||
Evaluation metrics |
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
- 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
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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) -
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)
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)
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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 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
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
quantitative estimate of protein-protein interaction targeting drug-likeness
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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
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
J Cheminform 1, 8 (2009) | code
Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning
Chemical Science 12.9 (2021) | code
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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 -
MolScore
An automated scoring function to facilitate and standardize evaluation of goal-directed generative models for de novo molecular design
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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 -
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
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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]
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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
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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
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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]
Liu, X., Ye, K., van Vlijmen, H.W.T. et al.
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 -
Translation between Molecules and Natural Language [2022]
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
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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 Monte Carlo Tree Search [2023]
Iwata, Hiroaki, Taichi Nakai, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, and Yasushi Okuno.
chemrxiv-2023-q8419-v2 -
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
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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]
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DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design [2023]
Guan, Jiaqi, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, and Quanquan Gu.
ICML (2023) | code -
LS-MolGen: Ligand-and-Structure Dual-Driven Deep Reinforcement Learning for Target-Specific Molecular Generation Improves Binding Affinity and Novelty [2023]
Li, Song, Chao Hu, Song Ke, Chenxing Yang, Jun Chen, Yi Xiong, Hao Liu, and Liang Hong.
J. Chem. Inf. Model. (2023) | code -
Accelerating drug target inhibitor discovery with a deep generative foundation model [2023]
Vijil Chenthamarakshan et al.
Sci. Adv.9,eadg7865(2023) | code -
A Simple Way to Incorporate Target Structural Information in Molecular Generative Models [2023]
Zhang, Wenyi, Kaiyue Zhang, and Jing Huang.
Journal of Chemical Information and Modeling (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 -
Mol-Zero-GAN: Zero-Shot Adaptation of Molecular Generative Adversarial Network for Specific Protein Targets [2023]
Ravipas Aphikulvanich*, Natapol Pornputtapong, Duangdao Wichadakul
chemrxiv-2023-lv2m1 | code -
Molecule Generation For Target Protein Binding with Structural Motifs [2023]
Zhang, Zaixi, Yaosen Min, Shuxin Zheng, and Qi Liu.
The Eleventh International Conference on Learning Representations. (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 -
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction [2023]
Guan, Jiaqi, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma.
The Eleventh International Conference on Learning Representations. (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:2302.07868 (2023) -
Structure-based Drug Design with Equivariant Diffusion Models [2023]
Schneuing, A., Du, Y., Harris, C., Jamasb, A., Igashov, I., Du, W., ... & Correia, B.
arXiv:2210.13695 (2022) | code -
A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design [2022]
Chan, Lucian, Rajendra Kumar, Marcel Verdonk, and Carl Poelking.
Nat Mach Intell 4, 1130–1142 (2022) | code -
Reinforced Genetic Algorithm for Structure-based Drug Design [2022]
Fu, Tianfan, Wenhao Gao, Connor Coley, and Jimeng Sun.
Advances in Neural Information Processing Systems 35 (2022) | code -
Exploiting pretrained biochemical language models for targeted drug design [2022]
Uludoğan, Gökçe, Elif Ozkirimli, Kutlu O. Ulgen, Nilgün Karalı, and Arzucan Özgür.
Bioinformatics 38.Supplement_2 (2022) | 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.,
Journal of Medicinal Chemistry 65.13 (2022) | 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 (2022) | code -
De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning [2022]
Bontha M, McNaughton A, Knutson C, Pope J, Kumar N.
arXiv:2205.10473 (2022) -
AlphaDrug: protein target specific de novo molecular generation [2022]
Qian, Hao, Cheng Lin, Dengwei Zhao, Shikui Tu, and Lei Xu.
PNAS Nexus 1.4 (2022) | code -
LIMO: Latent Inceptionism for Targeted Molecule Generation [2022]
Eckmann, Peter, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, and Rose Yu.
arXiv:2206.09010 (2022) | code -
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets [2022]
Peng, Xingang, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma.
International Conference on Machine Learning. PMLR, (2022) | code -
Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors [2022]
Jeon, W., Kim, D.
Sci Rep 10, 22104 (2020) | code -
Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking [2022]
Eguida, Merveille, Christel Schmitt-Valencia, Marcel Hibert, Pascal Villa, and Didier Rognan.
Journal of Medicinal Chemistry 65.20 (2022) | code -
Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration [2022]
Hadfield, Thomas E., Fergus Imrie, Andy Merritt, Kristian Birchall, and Charlotte M. Deane.
J. Chem. Inf. Model. 2022, 62, 10, 2280–2292 | code -
Fragment-Based Ligand Generation Guided By Geometric Deep Learning On Protein-Ligand Structure [2022]
Powers, Alexander S., Helen H. Yu, Patricia Suriana, and Ron O. Dror.
bioRxiv (2022) -
Zero-Shot 3D Drug Design by Sketching and Generating [2022]
Long, Siyu, Yi Zhou, Xinyu Dai, and Hao Zhou.
arXiv:2209.13865 (2022) | code -
Structure-based de novo drug design using 3D deep generative models [2021]
Li, Yibo, Jianfeng Pei, and Luhua Lai.
Chemical science 12.41 (2021) -
Transformer neural network for protein-specific de novo drug generation as a machine translation proble [2021]
Grechishnikova, Daria.
Sci Rep 11, 321 (2021) | code -
Structure-aware generation of drug-like molecules [2021]
Drotár, P., Jamasb, A.R., Day, B., Cangea, C. and Liò, P.,
arXiv:2111.04107 (2021) -
A 3D Generative Model for Structure-Based Drug Design [2021]
Luo, S., Guan, J., Ma, J., & Peng, J.
Advances in Neural Information Processing Systems 34 (2021) | code -
Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations [2021]
Ma, B., Terayama, K., Matsumoto, S., Isaka, Y., Sasakura, Y., Iwata, H., Araki, M. and Okuno, Y.
J. Chem. Inf. Model. 2021, 61, 7, 3304–3313 | code
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DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning [2023]
Liu, X., Ye, K., van Vlijmen, H.W.T. et al.
J Cheminform 15, 24 (2023) | code -
Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer [2023]
Zhirui Liao, Lei Xie, Hiroshi Mamitsuka, Shanfeng Zhu.
Bioinformatics 39.1 (2023) | code -
De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning [2022]
Bontha M, McNaughton A, Knutson C, Pope J, Kumar N.
arXiv:2205.10473 (2022) -
LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design [2022]
Fialková, V., Zhao, J., Papadopoulos, K., Engkvist, O., Bjerrum, E.J., Kogej, T. and Patronov, A
J. Chem. Inf. Model. 2022, 62, 9, 2046–2063 | code -
Learning to Extend Molecular Scaffolds with Structural Motifs [2022]
Maziarz, Krzysztof, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt.
arXiv:2103.03864 (2021) -
Deep scaffold hopping with multimodal transformer neural networks [2021]
Zheng, Shuangjia, Zengrong Lei, Haitao Ai, Hongming Chen, Daiguo Deng, and Yuedong Yang.
J Cheminform 13, 87 (2021) | code -
Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches [2021]
Hu, Lizhao, Yuyao Yang, Shuangjia Zheng, Jun Xu, Ting Ran, and Hongming Chen.
J. Chem. Inf. Model. 2021, 61, 10, 4900–4912 | code -
3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds [2021]
Joshi, Rajendra P., Niklas WA Gebauer, Mridula Bontha, Mercedeh Khazaieli, Rhema M. James, James B. Brown, and Neeraj Kumar.
J. Phys. Chem. B 2021, 125, 44, 12166–12176 | code -
SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design [2020]
Arús-Pous, Josep, Atanas Patronov, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist.
J Cheminform 12, 38 (2020) | chemrxiv.11638383.v1 | code -
Scaffold-based molecular design with a graph generative model [2020]
Lim, Jaechang, Sang-Yeon Hwang, Seokhyun Moon, Seungsu Kim, and Woo Youn Kim.
Chemical science 11.4 (2020) | code
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MAGNet: Motif-Agnostic Generation of Molecules from Shapes [2023]
Hetzel, Leon, Johanna Sommer, Bastian Rieck, Fabian Theis, and Stephan Günnemann.
arXiv:2305.19303 (2023) -
Molecule Generation For Target Protein Binding with Structural Motifs [2023]
Zhang, Zaixi, Yaosen Min, Shuxin Zheng, and Qi Liu.
The Eleventh International Conference on Learning Representations. (2023) | code -
De Novo Molecular Generation via Connection-aware Motif Mining [2023]
Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu
arXiv:2302.01129 (2023) | code -
Learning to Extend Molecular Scaffolds with Structural Motifs [2022]
Maziarz, Krzysztof, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt.
arXiv:2103.03864 (2021) -
Hierarchical generation of molecular graphs using structural motifs [2020]
Jin, Wengong, Regina Barzilay, and Tommi Jaakkola.
International conference on machine learning. PMLR, (2020) | code
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Fragment-based Molecule Design with Self-learning Entropic Population Annealing [2023]
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, 63, 11, 3319–3327 | code -
Tree-Invent: A novel molecular generative model constrained with topological tree [2023]
Mingyuan Xu, HongMing Chen.
chemrxiv-2023-m77vk | code -
MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities [2023]
Yanyan Diao, Feng Hu, Zihao Shen, Honglin Li*.
Bioinformatics (2023) | code -
Fragment-based Deep Molecular Generation using Hierarchical Chemical Graph Representation and Multi-Resolution Graph Variational Autoencoder [2023]
Gao, Zhenxiang, Xinyu Wang, Blake Blumenfeld Gaines, Xuetao Shi, Jinbo Bi, and Minghu Song.
Molecular Informatics (2023) -
Fragment-based t-SMILES for de novo molecular generation [2023]
Wu, Juan-Ni, Tong Wang, Yue Chen, Li-Juan Tang, Hai-Long Wu, and Ru-Qin Yu.
arXiv:2301.01829 (2023) | code -
Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking [2022]
Eguida, Merveille, Christel Schmitt-Valencia, Marcel Hibert, Pascal Villa, and Didier Rognan.
Journal of Medicinal Chemistry 65.20 (2022): 13771-13783 | code -
Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration [2022]
Hadfield, Thomas E., Fergus Imrie, Andy Merritt, Kristian Birchall, and Charlotte M. Deane.
J. Chem. Inf. Model. 2022, 62, 10, 2280–2292 | code -
Fragment-Based Ligand Generation Guided By Geometric Deep Learning On Protein-Ligand Structure [2022]
Powers, Alexander S., Helen H. Yu, Patricia Suriana, and Ron O. Dror.
bioRxiv (2022) -
FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery [2022]
Pham, Thai-Hoang, Lei Xie, and Ping Zhang.
SDM. Society for Industrial and Applied Mathematics, (2022) -
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning [2022]
Flam-Shepherd, Daniel, Alexander Zhigalin, and Alán Aspuru-Guzik.
arXiv:2202.00658 (2022) -
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation [2021]
Yang, S., Hwang, D., Lee, S., Ryu, S., & Hwang, S. J.
Advances in Neural Information Processing Systems 34 (2021) | code -
Automated Generation of Novel Fragments Using Screening Data, a Dual SMILES Autoencoder, Transfer Learning and Syntax Correction [2021]
Bilsland, Alan E., Kirsten McAulay, Ryan West, Angelo Pugliese, and Justin Bower.
J. Chem. Inf. Model. 2021, 61, 6, 2547–2559 | code -
A Deep Generative Model for Fragment-Based Molecule Generation [2020]
Podda, Marco, Davide Bacciu, and Alessio Micheli.
International Conference on Artificial Intelligence and Statistics. PMLR, (2020) | code -
Multi-Objective Molecule Generation using Interpretable Substructures [2020]
Jin, Wengong, Regina Barzilay, and Tommi Jaakkola.
International conference on machine learning. PMLR, (2020) | 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:1910.13325 (2019) | code
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Reinforcement Learning-Driven Linker Design via Fast Attention-based Point Cloud Alignment [2023]
Neeser, Rebecca M., Mehmet Akdel, Daniel Kovtun, and Luca Naef.
arXiv:2306.08166 (2023) | code -
Fragment Linker Prediction Using the Deep Encoder-Decoder Network for PROTACs Drug Design [2023]
Kao, Chien-Ting, Chieh-Te Lin, Cheng-Li Chou, and Chu-Chung Lin.
J. Chem. Inf. Model. 2023, 63, 10, 2918–2927 | code -
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Desig [2023]
Igashov, I., Stärk, H., Vignac, C., Satorras, V.G., Frossard, P., Welling, M., Bronstein, M. and Correia, B.,
arXiv:2210.05274 (2022) | code -
DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design [2022]
Tan, Y., Dai, L., Huang, W., Guo, Y., Zheng, S., Lei, J., ... & Yang, Y.
J. Chem. Inf. Model. 2022, 62, 23, 5907–5917 | code -
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design [2022]
Huang, Yinan, Xingang Peng, Jianzhu Ma, and Muhan Zhang.
arXiv:2205.07309 (2022) | code -
SyntaLinker-Hybrid: A deep learning approach for target specific drug design [2022]
Feng, Yu, Yuyao Yang, Wenbin Deng, Hongming Chen, and Ting Ran.
Artificial Intelligence in the Life Sciences 2 (2022) -
Deep Generative Models for 3D Linker Design [2020]
Imrie, Fergus, Anthony R. Bradley, Mihaela van der Schaar, and Charlotte M. Deane.
J. Chem. Inf. Model. 2020, 60, 4, 1983–1995 | code -
SyntaLinker: automatic fragment linking with deep conditional transformer neural networks [2020]
Yang, Yuyao, Shuangjia Zheng, Shimin Su, Chao Zhao, Jun Xu, and Hongming Chen.
Chemical science 11.31 (2020) | code
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Uni-RXN: A Unified Framework Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation [2023]
Bo Qiang, Yiran Zhou, Yuheng Ding, Ningfeng Liu, Song Song, Liangren Zhang, Bo Huang, Zhenming Liu
arXiv:2303.06965 (2023) | code -
Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly [2023]
Seo, Seonghwan, Jaechang Lim, and Woo Youn Kim.
Advanced Science (2023) | code -
Synthesis-Aware Generation of Structural Analogues [2022]
Dolfus, Uschi, Hans Briem, and Matthias Rarey.
J. Chem. Inf. Model. 2022, 62, 15, 3565–3576 | code -
ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery [2022]
Wang, Jike, Xiaorui Wang, Huiyong Sun, Mingyang Wang, Yundian Zeng, Dejun Jiang, Zhenxing Wu et al.
Journal of Medicinal Chemistry 65.18 (2022) | code -
Generating reaction trees with cascaded variational autoencoders [2022]
Nguyen, Dai Hai, and Koji Tsuda.
The Journal of Chemical Physics 156.4 (2022) | code -
Synthesis-Aware Generation of Structural Analogues [2022]
Dolfus, Uschi, Hans Briem, and Matthias Rarey.
J. Chem. Inf. Model. 2022, 62, 15, 3565–3576 -
SynthI: A New Open-Source Tool for Synthon-Based Library Design [2022]
Zabolotna, Yuliana, Dmitriy M. Volochnyuk, Sergey V. Ryabukhin, Kostiantyn Gavrylenko, Dragos Horvath, Olga Klimchuk, Oleksandr Oksiuta, Gilles Marcou, and Alexandre Varnek.
J. Chem. Inf. Model. 2022, 62, 9, 2151–2163 | code -
Integrating Synthetic Accessibility with AI-based Generative Drug Design [2021]
Parrot, Maud, Hamza Tajmouati, Vinicius Barros Ribeiro da Silva, Brian Atwood, Robin Fourcade, Yann Gaston-Mathé, Nicolas Do Huu, and Quentin Perron.
chemrxiv-2021-jkhzw-v2 | code
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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 -
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, 63, 7, 1882–1893 -
PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning [2021]
Born, Jannis, Matteo Manica, Ali Oskooei, Joris Cadow, Greta Markert, and María Rodríguez Martínez.
Iscience 24.4 (2021) | code -
Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders [2020]
Shayakhmetov, Rim, Maksim Kuznetsov, Alexander Zhebrak, Artur Kadurin, Sergey Nikolenko, Alexander Aliper, and Daniil Polykovskiy.
Frontiers in Pharmacology (2020) | code -
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence [2020]
Méndez-Lucio, Oscar, Benoit Baillif, Djork-Arné Clevert, David Rouquié, and Joerg Wichard.
Nat Commun 11, 10 (2020)
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MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization [2022]
Sun, Mengying, Jing Xing, Han Meng, Huijun Wang, Bin Chen, and Jiayu Zhou.
KDD '2022 | code -
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 -
Multi-Objective Molecule Generation using Interpretable Substructures [2020]
Jin, Wengong, Regina Barzilay, and Tommi Jaakkola.
ICML (2020) | code -
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach [2020]
Khemchandani, Yash, Stephen O’Hagan, Soumitra Samanta, Neil Swainston, Timothy J. Roberts, Danushka Bollegala, and Douglas B. Kell.
J Cheminform 12, 53 (2020) | code -
Multi-objective de novo drug design with conditional graph generative model [2018]
Li, Yibo, Liangren Zhang, and Zhenming Liu.
J Cheminform 10, 33 (2018) | code
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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) -
Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry [2023]
Kao, Po-Yu, Ya-Chu Yang, Wei-Yin Chiang, Jen-Yueh Hsiao, Yudong Cao, Alex Aliper, Feng Ren et al.
J. Chem. Inf. Model. 2023, 63, 11, 3307–3318 | code -
Quantum Generative Models for Small Molecule Drug Discovery [2021]
Li, Junde, Rasit O. Topaloglu, and Swaroop Ghosh.
IEEE Transactions on Quantum Engineering (2021) | code
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An end-to-end deep learning framework for translating mass spectra to de-novo molecules [2023]
Litsa, E.E., Chenthamarakshan, V., Das, P. et al.
Commun Chem 6, 132 (2023) | code -
MSNovelist: de novo structure generation from mass spectra [2022]
Stravs, M.A., Dührkop, K., Böcker, S. et al
Nat Methods 19, 865–870 (2022) | code
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Zhang, Jinzhe, Kei Terayama, Masato Sumita, Kazuki Yoshizoe, Kengo Ito, Jun Kikuchi, and Koji Tsuda
Science and technology of advanced materials 21.1 (2020) | code