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 *

183 related articles for article (PubMed ID: 34310133)

  • 21. Perspective: Machine learning potentials for atomistic simulations.
    Behler J
    J Chem Phys; 2016 Nov; 145(17):170901. PubMed ID: 27825224
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Hybrid Perovskites, Metal-Organic Frameworks, and Beyond: Unconventional Degrees of Freedom in Molecular Frameworks.
    Boström HLB; Goodwin AL
    Acc Chem Res; 2021 Mar; 54(5):1288-1297. PubMed ID: 33600147
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties.
    Gallegos LC; Luchini G; St John PC; Kim S; Paton RS
    Acc Chem Res; 2021 Feb; 54(4):827-836. PubMed ID: 33534534
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations.
    Winter R; Montanari F; Noé F; Clevert DA
    Chem Sci; 2019 Feb; 10(6):1692-1701. PubMed ID: 30842833
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Wavelet scattering networks for atomistic systems with extrapolation of material properties.
    Sinz P; Swift MW; Brumwell X; Liu J; Kim KJ; Qi Y; Hirn M
    J Chem Phys; 2020 Aug; 153(8):084109. PubMed ID: 32872889
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Extending machine learning beyond interatomic potentials for predicting molecular properties.
    Fedik N; Zubatyuk R; Kulichenko M; Lubbers N; Smith JS; Nebgen B; Messerly R; Li YW; Boldyrev AI; Barros K; Isayev O; Tretiak S
    Nat Rev Chem; 2022 Sep; 6(9):653-672. PubMed ID: 37117713
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Efficient implementation of atom-density representations.
    Musil F; Veit M; Goscinski A; Fraux G; Willatt MJ; Stricker M; Junge T; Ceriotti M
    J Chem Phys; 2021 Mar; 154(11):114109. PubMed ID: 33752353
    [TBL] [Abstract][Full Text] [Related]  

  • 28. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer.
    Ko TW; Finkler JA; Goedecker S; Behler J
    Acc Chem Res; 2021 Feb; 54(4):808-817. PubMed ID: 33513012
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design.
    Janet JP; Duan C; Nandy A; Liu F; Kulik HJ
    Acc Chem Res; 2021 Feb; 54(3):532-545. PubMed ID: 33480674
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Machine Learning Force Fields: Recent Advances and Remaining Challenges.
    Poltavsky I; Tkatchenko A
    J Phys Chem Lett; 2021 Jul; 12(28):6551-6564. PubMed ID: 34242032
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning.
    Nguyen P; Loveland D; Kim JT; Karande P; Hiszpanski AM; Han TY
    J Chem Inf Model; 2021 May; 61(5):2147-2158. PubMed ID: 33899482
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Mathematical formalisms based on approximated kinetic representations for modeling genetic and metabolic pathways.
    Alves R; Vilaprinyo E; Hernádez-Bermejo B; Sorribas A
    Biotechnol Genet Eng Rev; 2008; 25():1-40. PubMed ID: 21412348
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Physically informed artificial neural networks for atomistic modeling of materials.
    Pun GPP; Batra R; Ramprasad R; Mishin Y
    Nat Commun; 2019 May; 10(1):2339. PubMed ID: 31138813
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces.
    Lamoureux PS; Choksi TS; Streibel V; Abild-Pedersen F
    Phys Chem Chem Phys; 2021 Oct; 23(38):22022-22034. PubMed ID: 34570139
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Survey on graph embeddings and their applications to machine learning problems on graphs.
    Makarov I; Kiselev D; Nikitinsky N; Subelj L
    PeerJ Comput Sci; 2021; 7():e357. PubMed ID: 33817007
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics.
    Hagler AT
    J Comput Aided Mol Des; 2019 Feb; 33(2):205-264. PubMed ID: 30506159
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank.
    Helfrecht BA; Gasparotto P; Giberti F; Ceriotti M
    Front Mol Biosci; 2019; 6():24. PubMed ID: 31058166
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence.
    Zubatiuk T; Isayev O
    Acc Chem Res; 2021 Apr; 54(7):1575-1585. PubMed ID: 33715355
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Metal-Organic Frameworks in Modern Physics: Highlights and Perspectives.
    Mezenov YA; Krasilin AA; Dzyuba VP; Nominé A; Milichko VA
    Adv Sci (Weinh); 2019 Sep; 6(17):1900506. PubMed ID: 31508274
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Data-Driven Strategies for Accelerated Materials Design.
    Pollice R; Dos Passos Gomes G; Aldeghi M; Hickman RJ; Krenn M; Lavigne C; Lindner-D'Addario M; Nigam A; Ser CT; Yao Z; Aspuru-Guzik A
    Acc Chem Res; 2021 Feb; 54(4):849-860. PubMed ID: 33528245
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.