OpenLB
Open Library of Bioscience

Application of Deep Learning for Studying NMDA Receptors.

Abstract

Keywords

References

1. Kadry H, Noorani B, Cucullo L (2020) A blood���brain barrier overview on structure, function, impairment, and biomarkers of integrity. Fluids Barriers CNS 17(1):69. https://doi.org/10.1186/s12987-020-00230-3 [DOI: 10.1186/s12987-020-00230-3]
2. Shaker B, Yu M-S, Song JS, Ahn S, Ryu JY, Oh K-S, Na D (2020) LightBBB: computational prediction model of blood���brain-barrier penetration based on LightGBM. Bioinformatics 37(8):1135���1139. https://doi.org/10.1093/bioinformatics/btaa918 [DOI: 10.1093/bioinformatics/btaa918]
3. Singh M, Divakaran R, Konda LSK, Kristam R (2020) A classification model for blood brain barrier penetration. J Mol Graph Model 96:107516. https://doi.org/10.1016/j.jmgm.2019.107516 [DOI: 10.1016/j.jmgm.2019.107516]
4. Liu L, Zhang L, Feng H, Li S, Liu M, Zhao J, Liu H (2021) Prediction of the blood���brain barrier (BBB) permeability of chemicals based on machine-learning and ensemble methods. Chem Res Toxicol 34(6):1456���1467. https://doi.org/10.1021/acs.chemrestox.0c00343 [DOI: 10.1021/acs.chemrestox.0c00343]
5. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz���min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977���5010. https://doi.org/10.1021/jm4004285 [DOI: 10.1021/jm4004285]
6. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inf 29(6���7):476���488. https://doi.org/10.1002/minf.201000061 [DOI: 10.1002/minf.201000061]
7. Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2694���2718. https://doi.org/10.3762/bjoc.12.267 [DOI: 10.3762/bjoc.12.267]
8. Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen KF (2022) Generative models for molecular discovery: recent advances and challenges. Wiley Interdiscip Rev: Comput Mol Sci 12(5):e1608. https://doi.org/10.1002/wcms.1608 [DOI: 10.1002/wcms.1608]
9. Zhang D, Bi H, Dai F-Z, Jiang W, Zhang L, Wang H (2022) DPA-1: pretraining of attention-based deep potential model for molecular simulation. arXiv preprint arXiv:220808236
10. Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, dos Santos Costa A, Fazel-Zarandi M, Sercu T, Candido S, Rives A (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637):1123���1130. https://doi.org/10.1126/science.ade2574 [DOI: 10.1126/science.ade2574]
11. Zhou G, Gao Z, Ding Q, Zheng H, Xu H, Wei Z, Zhang L, Guolin Ke G (2023) Uni-Mol: a universal 3D molecular representation learning framework. Published as a conference paper at the Eleventh International Conference on Learning Representations (ICLR). https://openreview.net/pdf?id=6K2RM6wVqKu
12. Lu S, Yao L, Chen X, Zheng H, He D, Ke G (2023) 3D molecular generation via virtual dynamics. arXiv preprint arXiv:230205847
13. Wu Z, Ramsundar B, Feinberg Evan N, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9(2):513���530. https://doi.org/10.1039/C7SC02664A [DOI: 10.1039/C7SC02664A]
14. Bacilieri M, Varano F, Deflorian F, Marini M, Catarzi D, Colotta V, Filacchioni G, Galli A, Costagli C, Kaseda C, Moro S (2007) Tandem 3D-QSARs approach as a valuable tool to predict binding affinity data: design of new Gly/NMDA receptor antagonists as a key study. J Chem Inf Model 47(5):1913���1922. https://doi.org/10.1021/ci7001846 [DOI: 10.1021/ci7001846]
15. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455���461. https://doi.org/10.1002/jcc.21334 [DOI: 10.1002/jcc.21334]

MeSH Term

Chemicals