Hansen KB, Yi F, Perszyk RE, Furukawa H, Wollmuth LP, Gibb AJ, et al. Structure, function, and allosteric modulation of NMDA receptors. J Gen Physiol. 2018;150:1081–105.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Traynelis SF, Wollmuth LP, McBain CJ, Menniti FS, Vance KM, Ogden KK, et al. Glutamate receptor ion channels: structure, regulation, and function. Pharmacol Rev. 2010;62:405–96.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Newcomer JW, Farber NB, Olney JW. NMDA receptor function, memory, and brain aging. Dialogues Clin Neurosci. 2000;2:219–32.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Grand T, Abi Gerges S, David M, Diana MA, Paoletti P. Unmasking GluN1/GluN3A excitatory glycine NMDA receptors. Nat Commun. 2018;9:4769.

Article  PubMed  PubMed Central  Google Scholar 

Galliano E, Schonewille M, Peter S, Rutteman M, Houtman S, Jaarsma D, et al. Impact of NMDA receptor overexpression on cerebellar Purkinje cell activity and motor learning. eNeuro. 2018;5:ENEURO.0270-17.2018.

Karakas E, Furukawa H. Crystal structure ofa heterotetrameric NMDA receptor ion channel. Science. 2014;344:992–7.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Ma T, Cheng Q, Chen C, Luo Z, Feng D. Excessive activation of NMDA receptors in the pathogenesis of multiple peripheral organs via mitochondrial dysfunction, oxidative stress, and inflammation. SN Compr Clin Med. 2020;2:551–69.

Article  CAS  Google Scholar 

Parsons CG, Danysz W, Quack G. Memantine is a clinically well tolerated N-methyl-D-aspartate (NMDA) receptor antagonist—a review of preclinical data. Neuropharmacology. 1999;38:735–67.

Article  PubMed  CAS  Google Scholar 

Chatterton JE, Awobuluyi M, Premkumar LS, Takahashi H, Talantova M, Shin Y, et al. Excitatory glycine receptors containing the NR3 family of NMDA receptor subunits. Nature. 2002;415:793–8.

Article  PubMed  CAS  Google Scholar 

Bossi S, Pizzamiglio L, Paoletti P. Excitatory GluN1/GluN3A glycine receptors (eGlyRs) in brain signaling. Trends Neurosci. 2023;46:667–81.

Article  PubMed  CAS  Google Scholar 

Zeng Y, Zheng Y, Zhang T, Ye F, Zhan L, Kou Z, et al. Identification of a subtype-selective allosteric inhibitor of GluN1/GluN3 NMDA receptors. Front Pharmacol. 2022; 13.

Wang W, Gao X. Deep learning in bioinformatics. Methods 2019;166:1–3.

Article  PubMed  Google Scholar 

Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18:463–77.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Lavecchia A. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discov Today. 2019;24:2017–32.

Article  PubMed  Google Scholar 

Lo YC, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23:1538–46.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Carpenter KA, Cohen DS, Jarrell JT, Huang X. Deep learning and virtual drug screening. Future Med Chem. 2018;10:2557–67.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Oliveira T, Silva M, Maia E, Silva A, Taranto A. Virtual screening algorithms in drug discovery: a review focused on machine and deep learning methods. Drugs Drug Candidates. 2023;2:311–34.

Article  Google Scholar 

Koh HY, Nguyen ATN, Pan S, May LT, Webb GI. Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data. Nat Mach Intell. 2024;6:673–87.

Article  Google Scholar 

Ozturk H, Ozgur A, Ozkirimli E. DeepDTA: deep drug-target binding affinity prediction. Bioinformatics. 2018;34:i821–i9.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics. 2021;37:1140–7.

Article  PubMed  CAS  Google Scholar 

Bai P, Miljković F, John B, Lu H. Interpretable bilinear attention network with domain adaptation improves drug–target prediction. Nat Mach Intell. 2023;5:126–36.

Article  Google Scholar 

Lu W, Wu Q, Zhang J, Rao J, Li C, Zheng S. Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction. Adv Neural Inf Process Syst. 2022;35:7236–49.

Google Scholar 

Zheng L, Fan J, Mu Y. OnionNet: a multiple-layer intermolecular-contact-based convolutional neural network for protein-ligand binding affinity prediction. ACS Omega. 2019;4:15956–65.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Wang Z, Zheng L, Liu Y, Qu Y, Li YQ, Zhao M, et al. OnionNet-2: A convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells. Front Chem. 2021;9:753002.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Nguyen DD, Wei GW. AGL-score: algebraic graph learning score for protein-ligand binding scoring, ranking, docking, and screening. J Chem Inf Model. 2019;59:3291–304.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Wang Z, Wang S, Li Y, Guo J, Wei Y, Mu Y, et al. A new paradigm for applying deep learning to protein-ligand interaction prediction. Brief Bioinform. 2024;25:bbae145.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Wang Z, Zheng L, Wang S, Lin M, Wang Z, Kong AW, et al. A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. Brief Bioinform. 2023;24:bbac520.

Article  PubMed  Google Scholar 

Zheng L, Meng J, Jiang K, Lan H, Wang Z, Lin M, et al. Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term. Brief Bioinform. 2022;23:bbac051.

Article  PubMed  PubMed Central  Google Scholar 

Shen T, Liu F, Wang Z, Sun J, Bu Y, Meng J, et al. zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15. Proteins. 2023;91:1837–49.

Article  PubMed  CAS  Google Scholar 

Méndez-Lucio O, Ahmad M, del Rio-Chanona EA, Wegner JK. A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat Mach Intell. 2021;3:1033–9.

Article  Google Scholar 

Shen C, Zhang X, Deng Y, Gao J, Wang D, Xu L, et al. Boosting protein-ligand binding pose prediction and virtual screening based on residue-atom distance likelihood potential and graph transformer. J Med Chem. 2022;65:10691–706.

Article  PubMed  CAS  Google Scholar 

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–9.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379:1123–30.

Article  PubMed  CAS  Google Scholar 

Edwards C, Lai T, Ros K, Honke G, Cho K, Ji H. Translation between molecules and natural language. Preprint at