403 related articles for article (PubMed ID: 37431910)
1. Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.
Zhang P; Yang W
J Chem Phys; 2023 Jul; 159(2):. PubMed ID: 37431910
[TBL] [Abstract][Full Text] [Related]
2. Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.
Wang H; Yang W
J Chem Theory Comput; 2019 Feb; 15(2):1409-1417. PubMed ID: 30550274
[TBL] [Abstract][Full Text] [Related]
3. Machine learning builds full-QM precision protein force fields in seconds.
Han Y; Wang Z; Wei Z; Liu J; Li J
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34017993
[TBL] [Abstract][Full Text] [Related]
4. Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.
Shen L; Yang W
J Chem Theory Comput; 2018 Mar; 14(3):1442-1455. PubMed ID: 29438614
[TBL] [Abstract][Full Text] [Related]
5. Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations.
Mackerell AD; Feig M; Brooks CL
J Comput Chem; 2004 Aug; 25(11):1400-15. PubMed ID: 15185334
[TBL] [Abstract][Full Text] [Related]
6. Building quantum mechanics quality force fields of proteins with the generalized energy-based fragmentation approach and machine learning.
Cheng Z; Du J; Zhang L; Ma J; Li W; Li S
Phys Chem Chem Phys; 2022 Jan; 24(3):1326-1337. PubMed ID: 34718360
[TBL] [Abstract][Full Text] [Related]
7. Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy.
Wang Z; Han Y; Li J; He X
J Phys Chem B; 2020 Apr; 124(15):3027-3035. PubMed ID: 32208716
[TBL] [Abstract][Full Text] [Related]
8. Molecular dynamics and quantum mechanics of RNA: conformational and chemical change we can believe in.
Ditzler MA; Otyepka M; Sponer J; Walter NG
Acc Chem Res; 2010 Jan; 43(1):40-7. PubMed ID: 19754142
[TBL] [Abstract][Full Text] [Related]
9. CHARMM additive and polarizable force fields for biophysics and computer-aided drug design.
Vanommeslaeghe K; MacKerell AD
Biochim Biophys Acta; 2015 May; 1850(5):861-871. PubMed ID: 25149274
[TBL] [Abstract][Full Text] [Related]
10. Classical and quantum mechanical/molecular mechanical molecular dynamics simulations of alanine dipeptide in water: comparisons with IR and vibrational circular dichroism spectra.
Kwac K; Lee KK; Han JB; Oh KI; Cho M
J Chem Phys; 2008 Mar; 128(10):105106. PubMed ID: 18345930
[TBL] [Abstract][Full Text] [Related]
11. Graph-convolutional neural networks for (QM)ML/MM molecular dynamics simulations.
Hofstetter A; Böselt L; Riniker S
Phys Chem Chem Phys; 2022 Sep; 24(37):22497-22512. PubMed ID: 36106790
[TBL] [Abstract][Full Text] [Related]
12. BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules.
Laghuvarapu S; Pathak Y; Priyakumar UD
J Comput Chem; 2020 Mar; 41(8):790-799. PubMed ID: 31845368
[TBL] [Abstract][Full Text] [Related]
13. On the faithfulness of molecular mechanics representations of proteins towards quantum-mechanical energy surfaces.
König G; Riniker S
Interface Focus; 2020 Dec; 10(6):20190121. PubMed ID: 33184586
[TBL] [Abstract][Full Text] [Related]
14. The CHARMM-TURBOMOLE interface for efficient and accurate QM/MM molecular dynamics, free energies, and excited state properties.
Riahi S; Rowley CN
J Comput Chem; 2014 Oct; 35(28):2076-86. PubMed ID: 25178266
[TBL] [Abstract][Full Text] [Related]
15. Comparison of a QM/MM force field and molecular mechanics force fields in simulations of alanine and glycine "dipeptides" (Ace-Ala-Nme and Ace-Gly-Nme) in water in relation to the problem of modeling the unfolded peptide backbone in solution.
Hu H; Elstner M; Hermans J
Proteins; 2003 Feb; 50(3):451-63. PubMed ID: 12557187
[TBL] [Abstract][Full Text] [Related]
16. Combined fragment-based machine learning force field with classical force field and its application in the NMR calculations of macromolecules in solutions.
Liao K; Dong S; Cheng Z; Li W; Li S
Phys Chem Chem Phys; 2022 Aug; 24(31):18559-18567. PubMed ID: 35916054
[TBL] [Abstract][Full Text] [Related]
17. Biomolecular force fields: where have we been, where are we now, where do we need to go and how do we get there?
Dauber-Osguthorpe P; Hagler AT
J Comput Aided Mol Des; 2019 Feb; 33(2):133-203. PubMed ID: 30506158
[TBL] [Abstract][Full Text] [Related]
18. AUTOMATED FORCE FIELD PARAMETERIZATION FOR NON-POLARIZABLE AND POLARIZABLE ATOMIC MODELS BASED ON
Huang L; Roux B
J Chem Theory Comput; 2013 Aug; 9(8):. PubMed ID: 24223528
[TBL] [Abstract][Full Text] [Related]
19. Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems.
Böselt L; Thürlemann M; Riniker S
J Chem Theory Comput; 2021 May; 17(5):2641-2658. PubMed ID: 33818085
[TBL] [Abstract][Full Text] [Related]
20. Accelerated Quantum Mechanics/Molecular Mechanics Simulations via Neural Networks Incorporated with Mechanical Embedding Scheme.
Zhou B; Zhou Y; Xie D
J Chem Theory Comput; 2023 Feb; 19(4):1157-1169. PubMed ID: 36724190
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
