BetaCitation Atlas

RESEARCH PAPER · Nature Communications · 2021

Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials

Yuanyuan Jiang, Zongwei Yang, Jiali Guo, Hongzhen Li, Yijing Liu, Yanzhi Guo, Menglong Li, Xuemei Pu

Read original paper ↗

See where this research leads.

117 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materialsOriginalD-P11 血中NBD-グルコサミンの蛍光について0Molecular set transformer: attending to the co-crystals in the Cambridge structural database29Deep learning modeling strategy for material science: from natural materials to metamaterials29Emerging Landscape of Computational Modeling in Pharmaceutical Development48Minoxidil Multi-Component Crystals with Aromatic Carboxylic Acids: Theoretical Calculation and Structural Analysis16Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration31TG-GNN: Transformer based Geometric enhancement graph neural network for molecular property prediction4Biomarkers discovery for endometrial cancer: A graph convolutional sample network method6Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement28Rational Screening of Cocrystals using Thermal Analysis: Benchmarking on Energetic Materials25iGROWL: Improved Group Detection With Link Prediction7Prediction and Construction of Energetic Materials Based on Machine Learning Methods53Ai-Enhanced Chemical Paradigm: From Molecular Graphs to Accurate Prediction and Mechanism0High-throughput design of energetic molecules45General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation30Neural Network Prediction Model of Cocrystal Melting Temperature Based on Molecular Descriptors and Graphs15Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals39Accelerated prediction of perovskite material properties with classical machine learning and graph neural network6Searching for the analogues of 1,1-dinitro-2,2-diamino ethylene (FOX-7) by high-throughput computation and machine learning11Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation14Predicting the Melting Point of Energetic Molecules Using a Learnable Graph Neural Fingerprint Model19Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning13Multi-type Features Graph Neural Networks for Cocrystals Prediction using API-Coformers Interactions0Pharmaceutical Cocrystal Discovery via 3D-SMINBR: A New Network Recommendation Tool Augmented by 3D Molecular Conformations4Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs52HMM-GDAN: Hybrid multi-view and multi-scale graph duplex-attention networks for drug response prediction in cancer19QSPR models for sublimation enthalpy of energetic compounds18A novel energetic cocrystal of DATNBI/TNT with low sensitivity and an unexpected polymorphic transition5In silico co-crystal design: Assessment of the latest advances21Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks22Speeding Up the Cocrystallization Process: Machine Learning-Combined Methods for the Prediction of Multicomponent Systems35Machine Learning Prediction of Flavonoid Cocrystal Formation Combined with Experimental Validation12Advances of machine learning in materials science: Ideas and techniques105AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism45Computational screening for prediction of co-crystals: method comparison and experimental validation30Enhancing chemistry-intuitive feature learning to improve prediction performance of optical properties16Cocrystals of polynitrogen compounds as a basis for promising energetic materials: crystal structure prediction methods, their experimental verification, and evaluation of cocrystal properties8Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties35Machine Learning-Guided Prediction of Cocrystals Using Point Cloud-Based Molecular Representation25PocketFlow is a data-and-knowledge-driven structure-based molecular generative model94Prediction of impact sensitivity and electrostatic spark sensitivity for energetic compounds by machine learning and density functional theory16Review on automated 2D material design10Artificial Intelligence Assisted Pharmaceutical Crystallization26Leveraging Deep Chemical Language Processing for Co-crystal Prediction0Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface4A Comprehensive Review on Theoretical Screening Methods for Pharmaceutical Cocrystals28SGNN-T: Space graph neural network coupled transformer for molecular property prediction4Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR31Understanding the Key Factors for Photoinduced Radical Generation in Crystalline Triphenylamines Using Experiment and Machine Learning1Recent Advances of Organic Cocrystals in Emerging Cutting‐Edge Properties and Applications82Recent Advances of Organic Cocrystals in Emerging Cutting‐Edge Properties and Applications19Cocrystal Prediction Tool (CCPT): A Web Server for Deep Learning-Assisted Cocrystal Screening and Density Evaluation5Interpretable and Physicochemical-Intuitive Deep Learning Approach for the Design of Thermal Resistance of Energetic Compounds6CMSSP: A Contrastive Mass Spectra-Structure Pretraining Model for Metabolite Identification19DVL-CC: A Novel Dual-View Learning Framework for Compound Cocrystal Prediction Boosted by View Consistency and Complementarity0Graph-based intermolecular interaction prediction enables rational design of co-crystals3High-throughput encapsulated nanodroplet screening for accelerated co-crystal discovery5Hybrid computational approaches for accurate crystalline density prediction of cyclo -pentazolate salts2Recent developments on co-crystal polymorphs: from formation to prediction9SVCPI: A Soft Voting Ensemble-Based Model for Compound-Protein Interaction Prediction2Exploring Coformer Substitution in Cocrystallization: Griseofulvin and Phenol Derivatives7DTF-diffusion: A 3D equivariant diffusion generation model based on ligand-target information fusion5Cocry-pred: A Dynamic Resource Propagation Method for Cocrystal Prediction2Cocrystal Formation Prediction: Hybrid GIN-Mordred Model Outperforms DFT-Based Methods7Caprolactam Cocrystals: Bridging Virtual Screening to Experimental Solid-State Forms and Stability1Recent advances in pharmaceutical cocrystal design: leveraging in-silico technologies for enhanced drug development0Virtual Screening of Pharmaceutical Cocrystals Using Machine Learning Algorithms10Deep Supramolecular Language Processing for Co‐Crystal Prediction2Deep Supramolecular Language Processing for Co‐Crystal Prediction12Study on the performance and efficient preparation of an ADN-based cocrystal by acoustic resonance mixing technology6Resources for Accelerating Medicine Discovery0Ammonium Organosulfonates as Machine Learning-Driven “Crystallization Cocktails” for the Structure Determination of Liquid Molecules2From Binary to Higher‐Order Organic Cocrystals: Design Principles and Performance Optimization4From Binary to Higher‐Order Organic Cocrystals: Design Principles and Performance Optimization16Synergistically enhanced safety and energy density of energetic materials via interfacial constraint6Farthest point sampling in property designated chemical feature space as an effective strategy for enhancing the machine learning model performance for small scale chemical dataset9MFPLI: A Computational Framework for Assessing Biological Authenticity of Protein-Ligand Interactions Using Molecular Fingerprints and Structural Features0Integrating AI into OLED material design: a comprehensive review of computational frameworks, challenges, and opportunities16A Combined Experimental and Modeling Workflow to Tune Surface Properties of Organic Materials via Cocrystallization8The Structural Regulation and Properties of Energetic Materials: A Review10The Structure of Nitro‐ and Polynitropyrazoles: X‐Ray Crystallography Versus Theoretical Calculations5Ever-Increasing Role of Computational Tools in Solid-State Pharmaceutics: Advancing Drug Development with Enhanced Molecular Understanding and Risk Assessment7Data-efficient Machine Learning for Polymer Informatics12Machine learning-driven discovery of multicomponent pharmaceutical solid forms via DualNet: confidence-aware prediction and ranking of salts and cocrystals6From traditional screening to machine learning facilitated development of pharmaceutical cocrystals0Compound Cocrystal Prediction via Dual-View Learning Framework Under Adversarial Consistency and Complementarity Constraints0Interpretable machine learning for solvent prediction and mechanistic insights in multi-component crystal screening5Cocrystal Network-Based Recommendation: A Multi-Objective Random Walk-Based Network Model for Virtual Screening of Drug–Drug Cocrystals1Data-Driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening10Screening and validation of Sinomenine hydrochloride deep eutectic solvents ligand based on machine learning algorithm1Smart organic cocrystals: Recent advances and perspectives14Hydrogen bond strength for riparins (I-III) cocrystals with carboxylic acid group coformers: ESP, QTAIM, and NBO analytical screening0Crystal engineering for poorly water-soluble drugs: From design to applications14Discovery of energetic ionic cocrystals via high-throughput virtual screening4Quality by Computational Design: Harnessing AI to Advance Rational Drug Development12Graph neural networks for soft sensors: Learning from process topology and operational data4Autonomous Laboratory Planning Agent for Solid-State Form Selection and Polymorph Screening0Choosing Co-Crystal Partners for Plant-Derived Drug Candidates Using Molecular Complementarity, Solid-State Interactions, Solubility Gain, and Developability Constraints0Development and applications of epitaxial heterostructures based on organic nanowires0Statistical integration of knowledge-based, artificial intelligence, and thermodynamic models for rational in silico coformer selection: A case study on an NSAID-skeletal muscle relaxant system0A federated graph learning method to realize multi-party collaboration for molecular discovery3Multi-modal feature fusion graph neural networks for intelligent classification of pharmaceutical solid forms: Cocrystals, salts, and physical mixtures1Artificial intelligence for energetic materials: design on the levels of molecule, crystal and composite2Property-Driven Cocrystal Discovery via Gaussian Processes and Active Learning2A Multimodal Semi-Supervised Learning Framework for Pharmaceutical Cocrystals Prediction0Explainable Machine Learning for Efficient Cocrystal Prediction: Leveraging Morgan Fingerprints to Decode Local Structure-Cocrystal Formation Relationships1Explainable machine learning prediction model and experimental verification of pharmaceutical multicomponent crystals0Systematic Cocrystal Exploration with a Methylation Homologue Series: Diclofenac with Glycine, Sarcosine, N , N -Dimethylglycine, and Betaine0Rational High-Throughput Screening of Cocrystals: An Explainable Deep Learning Framework Based on Morgan Fingerprints and Cross-Attention0A Multimodal Semi-Supervised Learning Framework for Pharmaceutical Cocrystals Prediction1Rational design and preparation of sorafenib Co-amorphous systems based on explainable Machine learning and quantum chemistry1Accelerating pharmaceutical cocrystal discovery via in silico screening: prediction of formation and properties0Adaptive Data Splitting Strategy to Enhance Knowledge Learning for Phosphorescence Lifetime Prediction0Machine learning strategies integrating knowledge transfer and a cascaded framework: Guiding the discovery of NIR-II organic cocrystals0Rational Coformer Screening of Multicomponent Crystals of Abiraterone Acetate Using Machine Learning (ML) and Multicomponent Hydrogen Bond Propensity (MCHBP): A Comprehensive Structural and Computational Investigation0Crystal engineering and crystal structure prediction0Transfer learning-based multimodal prediction for efficient screening of afatinib co-amorphous systems0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

117 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. D-P11 血中NBD-グルコサミンの蛍光について

    日本組織細胞化学会総会プログラムおよび抄録集 · 1993-10-28

    華 早崎, 正仁 渡辺, 正良 小西, 真久 島田

    Read original paper ↗ ·

  2. Molecular set transformer: attending to the co-crystals in the Cambridge structural database

    Digital Discovery · 2022-01-01

    Aikaterini Vriza, Ioana Şovago, Daniel E. Widdowson, Vitaliy A. Kurlin, P.A. Wood, Matthew Stephen Dyer

    Read original paper ↗ ·

  3. Deep learning modeling strategy for material science: from natural materials to metamaterials

    Journal of Physics Materials · 2022-02-28

    Wenwen Li, Pu Chen, Bo Xiong, Guandong Liu, Shuliang Dou, Yaohui Zhan, Zhiyuan Zhu, Tao Chu, Yao Li, Wei Ma

    Read original paper ↗ ·

  4. Emerging Landscape of Computational Modeling in Pharmaceutical Development

    Journal of Chemical Information and Modeling · 2022-02-28

    Yuriy A. Abramov, Guangxu Sun, Qun Zeng

    Read original paper ↗ ·

  5. Minoxidil Multi-Component Crystals with Aromatic Carboxylic Acids: Theoretical Calculation and Structural Analysis

    Crystal Growth & Design · 2022-04-28

    Yuehua Deng, Yuchen Chen, Fang Xie, Jingwen Tang, Rui Zhang, Huaiyu Yang, Yanbin Jiang, Shiyuan Liu

    Read original paper ↗ ·

  6. Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration

    Journal of Chemical Information and Modeling · 2022-08-02

    Chaoshu Duan, Xuyang Liu, Wensheng Cai, Xueguang Shao

    Read original paper ↗ ·

  7. TG-GNN: Transformer based Geometric enhancement graph neural network for molecular property prediction

    Research Square · 2022-08-02

    Taohong Zhang, Xuxu Guo, Hen Chen, Suli Fan, Qianqian Li, Saian Chen, Xueqiang Guo, Han Zheng

    Read original paper ↗ ·

  8. Biomarkers discovery for endometrial cancer: A graph convolutional sample network method

    Computers in Biology and Medicine · 2022-10-13

    Erman Wu, Xuemeng Fan, Tong Tang, Jingjing Li, Jiao Wang, Xingyun Liu, Zayatta Zungar, Jiaojiao Ren, Cong Wu, Bairong Shen

    Read original paper ↗ ·

  9. Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement

    Pharmaceutics · 2022-10-16

    Xiao Fu, Yinxiang Cheng, Jian‐Rong Wang, Dingyan Wang, Yuanyuan Zhang, Kaixian Chen, Xuefeng Mei, Xiaomin Luo

    Read original paper ↗ ·

  10. Rational Screening of Cocrystals using Thermal Analysis: Benchmarking on Energetic Materials

    Crystal Growth & Design · 2022-10-25

    Nikita V. Muravyev, Leonid L. Fershtat, Igor L'vovich Dalinger, Kyrill Yu. Suponitsky, Ivan V. Ananyev, Igor N. Melnikov

    Read original paper ↗ ·

  11. iGROWL: Improved Group Detection With Link Prediction

    IEEE Transactions on Biometrics Behavior and Identity Science · 2022-12-02

    Viktor Schmuck, Oya Çeliktutan

    Read original paper ↗ ·

  12. Prediction and Construction of Energetic Materials Based on Machine Learning Methods

    Molecules · 2022-12-31

    Xiaowei Zang, Xiang Zhou, Haitao Bian, Weiping Jin, Xuhai Pan, Juncheng Jiang, M. Yu. Koroleva, Ruiqi Shen

    Read original paper ↗ ·

  13. Ai-Enhanced Chemical Paradigm: From Molecular Graphs to Accurate Prediction and Mechanism

    SSRN Electronic Journal · 2023-01-01

    Yu Jiang, Jiang Yu, Zhi Zhen Huang, Wei He, Jie Yu, Jie Yu, Siwei Deng, Xiao Shao, Weiwei Zhu, Xiao Shao

    Read original paper ↗ ·

  14. High-throughput design of energetic molecules

    Journal of Materials Chemistry A · 2023-01-01

    Jian Liu, Shicao Zhao, Bowen Duan, Xudong He, Chunming Yang, Xuemei Pu, Xinben Zhang, Yonghao Xiao, Fude Nie, Wen Hui Qian, Geng Li, Chaoyang Zhang

    Read original paper ↗ ·

  15. General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation

    Journal of Chemical Information and Modeling · 2023-02-03

    Jiali Guo, Ming Sun, Xueyan Zhao, Chaojie Shi, Haoming Su, Yanzhi Guo, Xuemei Pu

    Read original paper ↗ ·

  16. Neural Network Prediction Model of Cocrystal Melting Temperature Based on Molecular Descriptors and Graphs

    Crystal Growth & Design · 2023-02-27

    Hong Yue, Jingtao Wang, Meijin Lu

    Read original paper ↗ ·

  17. Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals

    Pharmaceutics · 2023-03-03

    Artem O. Surov, Anna G. Ramazanova, Alexander P. Voronin, Ksenia V. Drozd, Andrei V. Churakov, German L. Perlovich

    Read original paper ↗ ·

  18. Accelerated prediction of perovskite material properties with classical machine learning and graph neural network

    Materials Express · 2023-04-01

    Zhihao Dong, Youyong Li, Youyong Li

    Read original paper ↗ ·

  19. Searching for the analogues of 1,1-dinitro-2,2-diamino ethylene (FOX-7) by high-throughput computation and machine learning

    FirePhysChem · 2023-04-06

    Wen Hui Qian, Jing Huang, Shitai Guo, Bowen Duan, Weiyu Xie, Jian Liu, Chaoyang Zhang

    Read original paper ↗ ·

  20. Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation

    Crystal Growth & Design · 2023-05-03

    Florian Racher, Tom L. Petrick, Doris Elfriede Braun

    Read original paper ↗ ·

  21. Predicting the Melting Point of Energetic Molecules Using a Learnable Graph Neural Fingerprint Model

    The Journal of Physical Chemistry A · 2023-05-04

    Siwei Song, Yi Wang, Xiaolan Tian, Wei He, Fang Chen, Junnan Wu, Qinghua Zhang

    Read original paper ↗ ·

  22. Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning

    Energetic Materials Frontiers · 2023-05-26

    Wen Hui Qian, Jing Huang, Shitai Guo, Bowen Duan, Weiyu Xie, Jian Liu, Chaoyang Zhang

    Read original paper ↗ ·

  23. Multi-type Features Graph Neural Networks for Cocrystals Prediction using API-Coformers Interactions

    Research Square · 2023-06-07

    Medard Edmund Mswahili, Kyuri Jo, Seung-Dong Lee, Young-Seob Jeong

    Read original paper ↗ ·

  24. Pharmaceutical Cocrystal Discovery via 3D-SMINBR: A New Network Recommendation Tool Augmented by 3D Molecular Conformations

    Journal of Chemical Information and Modeling · 2023-07-03

    Lulu Zheng, Bin Zhu, Zengrui Wu, Mei Guo, Jinyao Chen, Minghuang Hong, Guixia Liu, Weihua Li, Guo‐Bin Ren, Yun Tang

    Read original paper ↗ ·

  25. Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs

    Pharmaceutics · 2023-08-22

    Yuehua Deng, Shiyuan Liu, Yanbin Jiang, Inês C. B. Martins, Thomas Rades

    Read original paper ↗ ·

  26. HMM-GDAN: Hybrid multi-view and multi-scale graph duplex-attention networks for drug response prediction in cancer

    Neural Networks · 2023-08-24

    Youfa Liu, Shufan Tong, Yongyong Chen

    Read original paper ↗ ·

  27. QSPR models for sublimation enthalpy of energetic compounds

    Chemical Engineering Journal · 2023-08-28

    Rui Liu, Rui Liu, Yuechuan Tang, Jie Mo Tian, Jing Huang, Chaoyang Zhang, Linyuan Wang, Jian Liu

    Read original paper ↗ ·

  28. A novel energetic cocrystal of DATNBI/TNT with low sensitivity and an unexpected polymorphic transition

    Journal of Applied Crystallography · 2023-09-01

    Zhiqiang Wang, Jinjiang Xu, Zhi‐Ping Zhao, Zhenqi Zhang, Yi Tong

    Read original paper ↗ ·

  29. In silico co-crystal design: Assessment of the latest advances

    Drug Discovery Today · 2023-09-07

    Carolina von Eßen, David Lüdeker

    Read original paper ↗ ·

  30. Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks

    ACS Omega · 2023-10-16

    Takuya Taniguchi, Mayuko Hosokawa, Toru Asahi

    Read original paper ↗ ·

  31. Speeding Up the Cocrystallization Process: Machine Learning-Combined Methods for the Prediction of Multicomponent Systems

    Crystal Growth & Design · 2023-10-19

    Rebecca Birolo, Federica Bravetti, Eugenio Alladio, Emanuele Priola, Gianluca Bianchini, Rubina Novelli, Andrea Aramini, Roberto Gobetto, Michele R. Chierotti

    Read original paper ↗ ·

  32. Machine Learning Prediction of Flavonoid Cocrystal Formation Combined with Experimental Validation

    Industrial & Engineering Chemistry Research · 2023-11-21

    Hong Yue, Jingtao Wang

    Read original paper ↗ ·

  33. Advances of machine learning in materials science: Ideas and techniques

    Frontiers of Physics · 2023-11-24

    Sue Sin Chong, Yi Sheng Ng, Hui‐Qiong Wang, Jin‐Cheng Zheng

    Read original paper ↗ ·

  34. AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism

    Journal of Hazardous Materials · 2023-12-23

    Zhi Zhen Huang, Yu Peng Jiang, Yu Peng Jiang, Wei He, Jie Yu, Jie Yu, Siwei Deng, Chun Yang, Weiwei Zhu, Xiao Shao

    Read original paper ↗ ·

  35. Computational screening for prediction of co-crystals: method comparison and experimental validation

    CrystEngComm · 2024-01-01

    Fateme Molajafari, Tianrui Li, Mehrnaz Abbasichaleshtori, Moein Hajian Z. D., Anthony F. Cozzolino, Daniel R. Fandrick, Joshua D. Howe

    Read original paper ↗ ·

  36. Enhancing chemistry-intuitive feature learning to improve prediction performance of optical properties

    Chemical Science · 2024-01-01

    Ming Sun, Caixia Fu, Haoming Su, Ruyue Xiao, Chaojie Shi, Zhiyun Lu, Xuemei Pu

    Read original paper ↗ ·

  37. Cocrystals of polynitrogen compounds as a basis for promising energetic materials: crystal structure prediction methods, their experimental verification, and evaluation of cocrystal properties

    Russian Chemical Bulletin · 2024-02-01

    Nikita М. Baraboshkin, Victor P. Zelenov, Dmitry V. Khakimov, A. V. Dzyabchenko, Tat’yana S. Pivina

    Read original paper ↗ ·

  38. Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties

    European Journal of Pharmaceutics and Biopharmaceutics · 2024-02-02

    Xiaoxiao Liang, Shiyuan Liu, Zebin Li, Yuehua Deng, Yanbin Jiang, Huaiyu Yang

    Read original paper ↗ ·

  39. Machine Learning-Guided Prediction of Cocrystals Using Point Cloud-Based Molecular Representation

    Chemistry of Materials · 2024-02-02

    Soroush Ahmadi, Mohammad Amin Ghanavati, Sohrab Rohani

    Read original paper ↗ ·

  40. PocketFlow is a data-and-knowledge-driven structure-based molecular generative model

    Nature Machine Intelligence · 2024-03-11

    Yuanyuan Jiang, Guo Zhang, Jing You, Hailin Zhang, Rui Yao, Huanzhang Xie, Liyun Zhang, Ziyi Xia, Mengzhe Dai, Yunjie Wu, Linli Li, Shengyong Yang

    Read original paper ↗ ·

  41. Prediction of impact sensitivity and electrostatic spark sensitivity for energetic compounds by machine learning and density functional theory

    Journal of Materials Science · 2024-05-01

    Qiong Wu, Xinyu Wang, Bin Yan, Shibao Luo, Xueming Zheng, Linghua Tan, Weihua Zhu

    Read original paper ↗ ·

  42. Review on automated 2D material design

    2D Materials · 2024-05-02

    Abdalaziz Rashid Al-Maeeni, Mikhail Lazarev, N. Kazeev, Kostya S. Novoselov, A. Ustyuzhanin

    Read original paper ↗ ·

  43. Artificial Intelligence Assisted Pharmaceutical Crystallization

    Crystal Growth & Design · 2024-05-03

    Zuoxuan Zhu, Yuan Zhang, Zhixuan Wang, Weiwei Tang, Jingkang Wang, Junbo Gong

    Read original paper ↗ ·

  44. Leveraging Deep Chemical Language Processing for Co-crystal Prediction

    ChemRxiv · 2024-05-29

    Rebecca Birolo, Rıza Özçelik, Andrea Aramini, Roberto Gobetto, Michele R. Chierotti, Francesca Grisoni

    Read original paper ↗ ·

  45. Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface

    AAPS PharmSciTech · 2024-06-11

    Yuting Wang, Yanling Jiang, Yu Zhou, He Huai, Jincao Tang, Anqing Luo, Zeng Liu, Chi Ma, Qin Xiao, Tianbing Guan, Chuanyun Dai

    Read original paper ↗ ·

  46. A Comprehensive Review on Theoretical Screening Methods for Pharmaceutical Cocrystals

    Journal of Molecular Structure · 2024-09-01

    J. Roshni, T. Karthick

    Read original paper ↗ ·

  47. SGNN-T: Space graph neural network coupled transformer for molecular property prediction

    Computational Materials Science · 2024-09-10

    Taohong Zhang, Chenglong Xia, Huguang Yang, Xuxu Guo, Han Zheng, Aziguli Wulamu

    Read original paper ↗ ·

  48. Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR

    Nature Communications · 2024-09-16

    Xin Chen, Kexin Wang, Jianfang Chen, Chao Wu, Jun Mao, Yuanpeng Song, Yijing Liu, Zhenhua Shao, Xuemei Pu

    Read original paper ↗ ·

  49. Understanding the Key Factors for Photoinduced Radical Generation in Crystalline Triphenylamines Using Experiment and Machine Learning

    The Journal of Physical Chemistry C · 2024-09-20

    Gamage Isuri P. Wijesekera, Fahidat Adedamola Gbadamosi, Muhammad Saddam Hossain, Abhilash Patra, Christopher A. Sutton, Linda S. Shimizu

    Read original paper ↗ ·

  50. Recent Advances of Organic Cocrystals in Emerging Cutting‐Edge Properties and Applications

    Angewandte Chemie International Edition · 2024-09-21

    Xin Wang, Zongrui Wang, Xiang Wang, Fangyuan Kang, Qianfeng Gu, Qichun Zhang

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.