These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

189 related articles for article (PubMed ID: 37581304)

  • 1. Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework.
    Yang J; Chen Z; Sun H; Samanta A
    J Chem Theory Comput; 2023 Sep; 19(17):5910-5923. PubMed ID: 37581304
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation.
    Zhang Y; Hu C; Jiang B
    J Phys Chem Lett; 2019 Sep; 10(17):4962-4967. PubMed ID: 31397157
    [TBL] [Abstract][Full Text] [Related]  

  • 3. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.
    Batzner S; Musaelian A; Sun L; Geiger M; Mailoa JP; Kornbluth M; Molinari N; Smidt TE; Kozinsky B
    Nat Commun; 2022 May; 13(1):2453. PubMed ID: 35508450
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Persistent homology-based descriptor for machine-learning potential of amorphous structures.
    Minamitani E; Obayashi I; Shimizu K; Watanabe S
    J Chem Phys; 2023 Aug; 159(8):. PubMed ID: 37606336
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multiphysical graph neural network (MP-GNN) for COVID-19 drug design.
    Li XS; Liu X; Lu L; Hua XS; Chi Y; Xia K
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35696650
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property predictions.
    Zhan H; Zhu X; Qiao Z; Hu J
    Anal Chim Acta; 2023 Mar; 1244():340558. PubMed ID: 36737143
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations.
    Park Y; Kim J; Hwang S; Han S
    J Chem Theory Comput; 2024 Jun; 20(11):4857-4868. PubMed ID: 38813770
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural Networks.
    Thürlemann M; Böselt L; Riniker S
    J Chem Theory Comput; 2022 Mar; 18(3):1701-1710. PubMed ID: 35112866
    [TBL] [Abstract][Full Text] [Related]  

  • 9. RotNet: A Rotationally Invariant Graph Neural Network for Quantum Mechanical Calculations.
    Tu H; Han Y; Wang Z; Chen A; Tao K; Ye S; Wang S; Wei Z; Li J
    Small Methods; 2024 Jan; 8(1):e2300534. PubMed ID: 37727096
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation.
    See TJ; Zhang D; Boley M; Chalmers DK
    J Chem Theory Comput; 2024 Oct; 20(20):8886-8896. PubMed ID: 39356714
    [TBL] [Abstract][Full Text] [Related]  

  • 11. MD-GNN: A mechanism-data-driven graph neural network for molecular properties prediction and new material discovery.
    Chen S; Wulamu A; Zou Q; Zheng H; Wen L; Guo X; Chen H; Zhang T; Zhang Y
    J Mol Graph Model; 2023 Sep; 123():108506. PubMed ID: 37182505
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron.
    Byggmästar J; Nikoulis G; Fellman A; Granberg F; Djurabekova F; Nordlund K
    J Phys Condens Matter; 2022 May; 34(30):. PubMed ID: 35550572
    [TBL] [Abstract][Full Text] [Related]  

  • 13. SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction.
    Zhang S; Jin Y; Liu T; Wang Q; Zhang Z; Zhao S; Shan B
    ACS Omega; 2023 Jun; 8(25):22496-22507. PubMed ID: 37396234
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints.
    Rahaman O; Gagliardi A
    J Chem Inf Model; 2020 Dec; 60(12):5971-5983. PubMed ID: 33118351
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions.
    Thürlemann M; Böselt L; Riniker S
    J Chem Theory Comput; 2023 Jan; 19(2):562-79. PubMed ID: 36633918
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition.
    Low K; Coote ML; Izgorodina EI
    J Chem Inf Model; 2022 Nov; 62(22):5457-5470. PubMed ID: 36317829
    [TBL] [Abstract][Full Text] [Related]  

  • 17. SLI-GNN: A Self-Learning-Input Graph Neural Network for Predicting Crystal and Molecular Properties.
    Dong Z; Feng J; Ji Y; Li Y
    J Phys Chem A; 2023 Jul; 127(28):5921-5929. PubMed ID: 37418164
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Accelerating atomistic simulations with piecewise machine-learned
    Zhang Y; Hu C; Jiang B
    Phys Chem Chem Phys; 2021 Jan; 23(3):1815-1821. PubMed ID: 33236743
    [TBL] [Abstract][Full Text] [Related]  

  • 19. TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias.
    Hedelius BE; Tingey D; Della Corte D
    J Chem Theory Comput; 2024 Jan; 20(1):199-211. PubMed ID: 38150692
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results.
    Hu F; He F; Yaron DJ
    J Chem Theory Comput; 2023 Sep; 19(18):6185-6196. PubMed ID: 37705220
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.