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Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements.
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
David Weininger. 1988 · 1988
Earlier work this paper cites.
International union of pure and applied chemistry
OF IUPAC. 1992 · 1992
Earlier work this paper cites.
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini and Yosef Hochberg. 1995 · 1995
Earlier work this paper cites.
Recap retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry
Xiao Qing Lewell, Duncan B Judd, Stephen P Watson, and Michael M Hann. 1998 · 1998
Earlier work this paper cites.
Reoptimization of MDL keys for use in drug discovery
Joseph L Durant, Burton A Leland, Douglas R Henry, and James G Nourse. 2002 · 2002
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics . 311–318
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Performance assessment of multiobjective optimizers: An analysis and review
Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M Fonseca, and Viviane Grunert Da Fonseca. 2003 · 2003
Earlier work this paper cites.
Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy
Richard A Friesner, Jay L Banks, Robert B Murphy, Thomas A Halgren, Jasna J Klicic, Daniel T Mainz, Matthew P Repasky, Eric H Knoll, Mee Shelley, Jason K Perry, et al · 2004
Earlier work this paper cites.
New Method for Fast and Accurate Binding-site Identification and Analysis
Tom Halgren. 2007 · 2007
Earlier work this paper cites.
Epik: a software program for pK a prediction and protonation state generation for drug-like molecules
John C Shelley, Anuradha Cholleti, Leah L Frye, Jeremy R Greenwood, Mathew R Timlin, and Makoto Uchimaya. 2007 · 2007
Earlier work this paper cites.
Levenshtein distance: Information theory, computer science, string (computer science), string metric, damerau? Levenshtein distance, spell checker, hamming distance
Frederic P Miller, Agnes F Vandome, and John McBrewster. 2009 · 2009
Earlier work this paper cites.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn. 2010 · 2010
Earlier work this paper cites.
On the properties of the R2 indicator. In Proceedings of the 14th annual conference on Genetic and evolutionary computation . 465–472
Dimo Brockhoff, Tobias Wagner, and Heike Trautmann. 2012 · 2012
Earlier work this paper cites.
Molecular docking and structure-based drug design strategies
Leonardo G Ferreira, Ricardo N Dos Santos, Glaucius Oliva, and Adriano D Andricopulo. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Get Your Atoms in Order An Open-Source Implementation of a Novel and Robust Molecular Canonicalization Algorithm
Nadine Schneider, Roger A Sayle, and Gregory A Landrum. 2015 · 2015
Earlier work this paper cites.
Structures of human A1 and A2A adenosine receptors with xanthines reveal determinants of selectivity
Robert KY Cheng, Elena Segala, Nathan Robertson, Francesca Deflorian, Andrew S Doré, James C Errey, Cédric Fiez-Vandal, Fiona H Marshall, and Robert M Cooke. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
A Vaswani. 2017 · 2017
Earlier work this paper cites.
Deep q-learning from demonstrations. In Proceedings of the AAAI conference on artificial intelligence , Vol. 32
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al · 2018
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation. In International conference on machine learning . PMLR, 2323–2332
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018 · 2018
Earlier work this paper cites.
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim, and Woo Youn Kim. 2018 · 2018
Earlier work this paper cites.
Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer. 2018 · 2018
Earlier work this paper cites.
SciBERT: A Pretrained Language Model for Scientific Text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3615–3620
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping
Boris Sattarov, Igor I Baskin, Dragos Horvath, Gilles Marcou, Esben Jannik Bjerrum, and Alexandre Varnek. 2019 · 2019
Cited alongside, same era.
Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley. 2019 · 2019
Cited alongside, same era.
Bidirectional molecule generation with recurrent neural networks
Francesca Grisoni, Michael Moret, Robin Lingwood, and Gisbert Schneider. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
Molsearch: search-based multi-objective molecular generation and property optimization. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining . 4724–4732
Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, and Jiayu Zhou. 2022 · 2022
Later among the works it cites.
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs. In International Conference on Learning Representations
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat. 2022 · 2022
Later among the works it cites.
Seqdiffuseq: Text diffusion with encoder-decoder transformers
Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Fei Huang, and Songfang Huang. 2022 · 2022
Later among the works it cites.
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Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks
Panagiotis-Christos Kotsias, Josep Arús-Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, and Esben Jannik Bjerrum. 2020 · 2020
Cited alongside, same era.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020 · 2020
Cited alongside, same era.
A deep-learning view of chemical space designed to facilitate drug discovery
Paul Maragakis, Hunter Nisonoff, Brian Cole, and David E Shaw. 2020 · 2020
Cited alongside, same era.
Mol-CycleGAN: a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, and Michał Warchoł. 2020 · 2020
Cited alongside, same era.
Moflow: an invertible flow model for generating molecular graphs. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 617–626
Chengxi Zang and Fei Wang. 2020 · 2020
Cited alongside, same era.
A deep generative model for molecule optimization via one fragment modification
Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, and Xia Ning. 2021 · 2021
Cited alongside, same era.
Mimosa: Multi-constraint molecule sampling for molecule optimization. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 125–133
Tianfan Fu, Cao Xiao, Xinhao Li, Lucas M Glass, and Jimeng Sun. 2021 · 2021
Cited alongside, same era.
Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining
Warith Eddine Djeddi, Khalil Hermi, Sadok Ben Yahia, and Gayo Diallo. 2023 · 2023
Later among the works it cites.
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. In The Eleventh International Conference on Learning Representations
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong. 2023 · 2023
Later among the works it cites.
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction. In The Eleventh International Conference on Learning Representations
Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma. 2023 · 2023
Later among the works it cites.
DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 4521–4534
Zhengfu He, Tianxiang Sun, Qiong Tang, Kuanning Wang, Xuan-Jing Huang, and Xipeng Qiu. 2023 · 2023
Later among the works it cites.
Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction
Yinghui Jiang, Shuting Jin, Xurui Jin, Xianglu Xiao, Wenfan Wu, Xiangrong Liu, Qiang Zhang, Xiangxiang Zeng, Guang Yang, and Zhangming Niu. 2023 · 2023
Later among the works it cites.
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models. In International Conference on Machine Learning . PMLR, 21450–21474
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley. 2023 · 2023
Later among the works it cites.
Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2023 · 2023
Later among the works it cites.
A survey on generative diffusion models
Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li. 2024 · 2024
Closest in time.
Text-guided molecule generation with diffusion language model. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 109–117
Haisong Gong, Qiang Liu, Shu Wu, and Liang Wang. 2024 · 2024
Closest in time.
A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets
Lei Huang, Tingyang Xu, Yang Yu, Peilin Zhao, Xingjian Chen, Jing Han, Zhi Xie, Hailong Li, Wenge Zhong, Ka-Chun Wong, et al · 2024
Closest in time.
A Tree-Transformer based VAE with fragment tokenization for large chemical models
Tensei Inukai, Aoi Yamato, Manato Akiyama, and Yasubumi Sakakibara. 2024 · 2024
Closest in time.
Zero-shot learning for preclinical drug screening. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence . 2117–2125
Kun Li, Weiwei Liu, Yong Luo, Xiantao Cai, Jia Wu, and Wenbin Hu. 2024 · 2024
Closest in time.
DyMol: Dynamic Many-Objective Molecular Optimization with Objective Decomposition and Progressive Optimization. In ICLR 2024 Workshop on Generative and Experimental Perspectives for Biomolecular Design
Dong-Hee Shin, Young-Han Son, Ji-Wung Han, Tae-Eui Kam, et al · 2024
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Structure-based, deep-learning models for protein-ligand binding affinity prediction
Debby D Wang, Wenhui Wu, and Ran Wang. 2024b · 2024
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A hierarchical attention network integrating multi-scale relationship for drug response prediction
Xiaoqi Wang, Yuqi Wen, Yixin Zhang, Chong Dai, Yaning Yang, Xiaochen Bo, Song He, and Shaoliang Peng. 2024a · 2024
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Pretraining graph transformer for molecular representation with fusion of multimodal information
Ruizhe Chen, Chunyan Li, Longyue Wang, Mingquan Liu, Shugao Chen, Jiahao Yang, and Xiangxiang Zeng. 2025 · 2025
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Graph-Structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities. In 2025 IEEE International Conference on Web Services (ICWS) . 1033–1042
Kun Li, Yida Xiong, Hongzhi Zhang, Xiantao Cai, Jia Wu, Bo Du, and Wenbin Hu. 2025a · 2025
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Contrastive learning-based drug screening model for GluN1/GluN3A inhibitors
Kun Li, Yue Zeng, Yi-da Xiong, Hao-chen Wu, Sui Fang, Zhi-yan Qu, Yan Zhu, Bo Du, Zhao-bing Gao, and Wen-bin Hu. 2025b · 2025
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Adaptive-weighted federated graph convolutional networks with multi-sensor data fusion for drug response prediction
Hui Yu, Qingyong Wang, and Xiaobo Zhou. 2025 · 2025
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