287 related articles for article (PubMed ID: 34081851)
41. BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening.
Brocidiacono M; Francoeur P; Aggarwal R; Popov KI; Koes DR; Tropsha A
J Chem Inf Model; 2024 Apr; 64(7):2488-2495. PubMed ID: 38113513
[TBL] [Abstract][Full Text] [Related]
42. FINDSITE
Zhou H; Cao H; Skolnick J
J Chem Inf Model; 2018 Nov; 58(11):2343-2354. PubMed ID: 30278128
[TBL] [Abstract][Full Text] [Related]
43. PharmaNet: Pharmaceutical discovery with deep recurrent neural networks.
Ruiz Puentes P; Valderrama N; González C; Daza L; Muñoz-Camargo C; Cruz JC; Arbeláez P
PLoS One; 2021; 16(4):e0241728. PubMed ID: 33901196
[TBL] [Abstract][Full Text] [Related]
44. DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.
Zhang H; Saravanan KM; Zhang JZH
Molecules; 2023 Jun; 28(12):. PubMed ID: 37375246
[TBL] [Abstract][Full Text] [Related]
45. Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking.
Shim H; Kim H; Allen JE; Wulff H
J Chem Inf Model; 2022 May; 62(10):2301-2315. PubMed ID: 35447030
[TBL] [Abstract][Full Text] [Related]
46. FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.
Zhou H; Skolnick J
Protein Sci; 2024 Jan; 33(1):e4869. PubMed ID: 38100293
[TBL] [Abstract][Full Text] [Related]
47. Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA.
Greenidge PA; Kramer C; Mozziconacci JC; Sherman W
J Chem Inf Model; 2014 Oct; 54(10):2697-717. PubMed ID: 25266271
[TBL] [Abstract][Full Text] [Related]
48. Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) Based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes.
Khashan R; Tropsha A; Zheng W
Mol Inform; 2022 Aug; 41(8):e2100248. PubMed ID: 35142086
[TBL] [Abstract][Full Text] [Related]
49. Employing Molecular Conformations for Ligand-Based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning.
Gu Y; Li J; Kang H; Zhang B; Zheng S
Molecules; 2023 Aug; 28(16):. PubMed ID: 37630234
[TBL] [Abstract][Full Text] [Related]
50. DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures.
Bao J; He X; Zhang JZH
J Chem Inf Model; 2021 May; 61(5):2231-2240. PubMed ID: 33979150
[TBL] [Abstract][Full Text] [Related]
51. Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening.
Jastrzębski S; Szymczak M; Pocha A; Mordalski S; Tabor J; Bojarski AJ; Podlewska S
J Chem Inf Model; 2020 Sep; 60(9):4246-4262. PubMed ID: 32865414
[TBL] [Abstract][Full Text] [Related]
52. Deffini: A family-specific deep neural network model for structure-based virtual screening.
Zhou D; Liu F; Zheng Y; Hu L; Huang T; Huang YS
Comput Biol Med; 2022 Dec; 151(Pt B):106323. PubMed ID: 36436482
[TBL] [Abstract][Full Text] [Related]
53. Enhancing generalizability and performance in drug-target interaction identification by integrating pharmacophore and pre-trained models.
Zhang Z; He X; Long D; Luo G; Chen S
Bioinformatics; 2024 Jun; 40(Supplement_1):i539-i547. PubMed ID: 38940179
[TBL] [Abstract][Full Text] [Related]
54. Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction.
Qu X; Dong L; Luo D; Si Y; Wang B
J Chem Inf Model; 2024 Apr; 64(7):2263-2274. PubMed ID: 37433009
[TBL] [Abstract][Full Text] [Related]
55. Novel Consensus Docking Strategy to Improve Ligand Pose Prediction.
Ren X; Shi YS; Zhang Y; Liu B; Zhang LH; Peng YB; Zeng R
J Chem Inf Model; 2018 Aug; 58(8):1662-1668. PubMed ID: 30044626
[TBL] [Abstract][Full Text] [Related]
56. GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking.
Baek M; Shin WH; Chung HW; Seok C
J Comput Aided Mol Des; 2017 Jul; 31(7):653-666. PubMed ID: 28623486
[TBL] [Abstract][Full Text] [Related]
57. ViTScore: A Novel Three-Dimensional Vision Transformer Method for Accurate Prediction of Protein-Ligand Docking Poses.
Guo L; Qiu T; Wang J
IEEE Trans Nanobioscience; 2023 Oct; 22(4):734-743. PubMed ID: 37159314
[TBL] [Abstract][Full Text] [Related]
58. Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.
Jacquemard C; Tran-Nguyen VK; Drwal MN; Rognan D; Kellenberger E
Molecules; 2019 Jul; 24(14):. PubMed ID: 31323745
[TBL] [Abstract][Full Text] [Related]
59. Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.
Cleves AE; Johnson SR; Jain AN
J Comput Aided Mol Des; 2019 Oct; 33(10):865-886. PubMed ID: 31650386
[TBL] [Abstract][Full Text] [Related]
60. Predicting protein-ligand binding residues with deep convolutional neural networks.
Cui Y; Dong Q; Hong D; Wang X
BMC Bioinformatics; 2019 Feb; 20(1):93. PubMed ID: 30808287
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
