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3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking.
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
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Binding moad (mother of all databases)
Liegi Hu, Mark L Benson, Richard D Smith, Michael G Lerner, and Heather A Carlson · 2005
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Sitehound-web: a server for ligand binding site identification in protein structures
Marylens Hernandez, Dario Ghersi, and Roberto Sanchez · 2009
Earlier work this paper cites.
Fpocket: An open source platform for ligand pocket detection
Vincent Le Guilloux, Peter Schmidtke, and Pierre Tuffery · 2009
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Chemberta: Large-scale self-supervised pretraining for molecular property prediction, 2020
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2010
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al · 2013
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
David Ryan Koes, Matthew P Baumgartner, and Carlos J Camacho · 2013
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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L Chevrier, Kristin A Persson, and Gerbrand Ceder · 2013
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Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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Deepsite: protein-binding site predictor using 3d-convolutional neural networks
José Jiménez, Stefan Doerr, Gerard Martínez-Rosell, Alexander S Rose, and Gianni De Fabritiis · 2017
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Protein–ligand scoring with convolutional neural networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Attention is all you need
A Vaswani · 2017
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O-cnn: Octree-based convolutional neural networks for 3d shape analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong · 2017
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Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Radoslav Krivák and David Hoksza · 2018
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Comparative assessment of scoring functions: the casf-2016 update
Minyi Su, Qifan Yang, Yu Du, Guoqin Feng, Zhihai Liu, Yan Li, and Renxiao Wang · 2018
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
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Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies
Gabriele Macari, Daniele Toti, and Fabio Polticelli · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy Alexey · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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Softflow: Probabilistic framework for normalizing flow on manifolds
Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, and Nam Soo Kim · 2020
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Discrete point flow networks for efficient point cloud generation
Roman Klokov, Edmond Boyer, and Jakob Verbeek · 2020
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Polygen: An autoregressive generative model of 3d meshes
Charlie Nash, Yaroslav Ganin, SM Ali Eslami, and Peter Battaglia · 2020
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Airss data for carbon at 10gpa and the c+n+h+o system at 1gpa, 2020
Chris J. Pickard · 2020
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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Glu variants improve transformer
Noam Shazeer · 2020
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
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Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings
Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli · 2021
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E (n) equivariant normalizing flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, and Max Welling · 2021
Earlier work this paper cites.
Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
Cited alongside, same era.
Setvae: Learning hierarchical composition for generative modeling of set-structured data
Jinwoo Kim, Jaehoon Yoo, Juho Lee, and Seunghoon Hong · 2021
Cited alongside, same era.
Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Cited alongside, same era.
A 3d generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 2021
Cited alongside, same era.
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G. Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, and Tonio Buonassisi · 2021
Cited alongside, same era.
polybert: a chemical language model to enable fully machine-driven ultrafast polymer informatics
Christopher Kuenneth and Rampi Ramprasad · 2023
Later among the works it cites.
Latent-nerf for shape-guided generation of 3d shapes and textures
Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or · 2023
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3d molecule generation by denoising voxel grids
Pedro O O Pinheiro, Joshua Rackers, Joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew Watkins, Stephen Ra, Vishnu Sresht, and Saeed Saremi · 2023
Later among the works it cites.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Openscene: 3d scene understanding with open vocabularies
Songyou Peng, Kyle Genova, Chiyu Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser, et al · 2023
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Dreamgaussian: Generative gaussian splatting for efficient 3d content creation
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof Schütt, Oliver Unke, and Michael Gastegger · 2021
Cited alongside, same era.
Octfield: Hierarchical implicit functions for 3d modeling
Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, and Lin Gao · 2021
Cited alongside, same era.
Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences
Karl DD Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik · 2021
Cited alongside, same era.
Deepcad: A deep generative network for computer-aided design models
Rundi Wu, Chang Xiao, and Changxi Zheng · 2021
Cited alongside, same era.
Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi S Jaakkola · 2021
Cited alongside, same era.
3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
Cited alongside, same era.
Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
Cited alongside, same era.
Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng · 2023
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Octformer: Octree-based transformers for 3d point clouds
Peng-Shuai Wang · 2023
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Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding
Le Xue, Mingfei Gao, Chen Xing, Roberto Martín-Martín, Jiajun Wu, Caiming Xiong, Ran Xu, Juan Carlos Niebles, and Silvio Savarese · 2023
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Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation
Zibo Zhao, Wen Liu, Xin Chen, Xianfang Zeng, Rui Wang, Pei Cheng, Bin Fu, Tao Chen, Gang Yu, and Shenghua Gao · 2023
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Uni-mol docking v2: Towards realistic and accurate binding pose prediction
Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li, Linfeng Zhang, Hang Zheng, and Gengmo Zhou · 2024
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Surfdock is a surface-informed diffusion generative model for reliable and accurate protein–ligand complex prediction
Duanhua Cao, Mingan Chen, Runze Zhang, Zhaokun Wang, Manlin Huang, Jie Yu, Xinyu Jiang, Zhehuan Fan, Wei Zhang, Hao Zhou, et al · 2024
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Dora: Sampling and benchmarking for 3d shape variational auto-encoders
Rui Chen, Jianfeng Zhang, Yixun Liang, Guan Luo, Weiyu Li, Jiarui Liu, Xiu Li, Xiaoxiao Long, Jiashi Feng, and Ping Tan · 2024
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Deep confident steps to new pockets: Strategies for docking generalization
Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, and Tommi Jaakkola · 2024
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Geometry-enhanced pretraining on interatomic potentials
Taoyong Cui, Chenyu Tang, Mao Su, Shufei Zhang, Yuqiang Li, Lei Bai, Yuhan Dong, Xingao Gong, and Wanli Ouyang · 2024
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Unigem: A unified approach to generation and property prediction for molecules
Shikun Feng, Yuyan Ni, Yan Lu, Zhi-Ming Ma, Wei-Ying Ma, and Yanyan Lan · 2024
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Meshformer: High-quality mesh generation with 3d-guided reconstruction model
Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, et al · 2024
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Flowmm: Generating materials with riemannian flow matching, 2024
Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, and Brandon M Wood · 2024
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Sequence modeling and design from molecular to genome scale with evo
Eric Nguyen, Michael Poli, Matthew G Durrant, Brian Kang, Dhruva Katrekar, David B Li, Liam J Bartie, Armin W Thomas, Samuel H King, Garyk Brixi, et al · 2024
Later among the works it cites.
3d molecule generation by denoising voxel grids
Pedro O O Pinheiro, Joshua Rackers, Joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew Watkins, Stephen Ra, Vishnu Sresht, and Saeed Saremi · 2024
Later among the works it cites.
Randar: Decoder-only autoregressive visual generation in random orders
Ziqi Pang, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T Freeman, and Yu-Xiong Wang · 2024
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Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies
Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams · 2024
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Meshgpt: Generating triangle meshes with decoder-only transformers
Yawar Siddiqui, Antonio Alliegro, Alexey Artemov, Tatiana Tommasi, Daniele Sirigatti, Vladislav Rosov, Angela Dai, and Matthias Nießner · 2024
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
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Visual autoregressive modeling: Scalable image generation via next-scale prediction
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang · 2024
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Mmpolymer: A multimodal multitask pretraining framework for polymer property prediction
Fanmeng Wang, Wentao Guo, Minjie Cheng, Shen Yuan, Hongteng Xu, and Zhifeng Gao · 2024
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Cad-mllm: Unifying multimodality-conditioned cad generation with mllm
Jingwei Xu, Chenyu Wang, Zibo Zhao, Wen Liu, Yi Ma, and Shenghua Gao · 2024
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Mol-ae: Auto-encoder based molecular representation learning with 3d cloze test objective
Junwei Yang, Kangjie Zheng, Siyu Long, Zaiqing Nie, Ming Zhang, Xinyu Dai, Wei-Ying Ma, and Hao Zhou · 2024
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Pre-training protein bi-level representation through span mask strategy on 3d protein chains
Jiale Zhao, Wanru Zhuang, Jia Song, Yaqi Li, and Shuqi Lu · 2024
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URL https://www.pdbbind-plus.org.cn/
Pdbbind+, 2025 · 2025
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End-to-end crystal structure prediction from powder x-ray diffraction
Qingsi Lai, Fanjie Xu, Lin Yao, Zhifeng Gao, Siyuan Liu, Hongshuai Wang, Lu Shuqi, Di He, Liwei Wang, Linfeng Zhang, Cheng Wang, and Guolin Ke · 2025
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Beyond atoms: Enhancing molecular pretrained representations with 3d space modeling
Shuqi Lu, Xiaohong Ji, Bohang Zhang, Lin Yao, Siyuan Liu, Zhifeng Gao, Linfeng Zhang, and Guolin Ke · 2025
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Naturelm: Deciphering the language of nature for scientific discovery
Yingce Xia, Peiran Jin, Shufang Xie, Liang He, Chuan Cao, Renqian Luo, Guoqing Liu, Yue Wang, Zequn Liu, Yuan-Jyue Chen, et al · 2025
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Unigenx: Unified generation of sequence and structure with autoregressive diffusion, 2025
Gongbo Zhang, Yanting Li, Renqian Luo, Pipi Hu, Zeru Zhao, Lingbo Li, Guoqing Liu, Zun Wang, Ran Bi, Kaiyuan Gao, Liya Guo, Yu Xie, Chang Liu, Jia Zhang, Tian Xie, Robert Pinsler, Claudio Zeni, Ziheng Lu, Yingce Xia, Marwin Segler, Maik Riechert, Li Yuan, Lei Chen, Haiguang Liu, and Tao Qin · 2025
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