biostudies-literatureEl Fadili MThis research was funded by the Researchers Supporting Project (No. RSPD2024R566), King Saud University, Riyadh, Saudi Arabia.King Saud University142https://www.ebi.ac.uk/biostudies/studies/S-EPMC11293250biostudies-literatureUnknown18(1)Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists, two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques, as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC₅₀ order) against neuropathic pain, with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), and artificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole momenBMC chemistryIn silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations.PMC11293250grant no. RSPD2024R566No. RSPD2024R566)Abuelizz HAKara MMujwar SElhallaoui MEl Fadili MEr-Rahmani SEr-Rajy MAjala ABouzammit RfalseIn silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations.Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists, two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques, as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC₅₀ order) against neuropathic pain, with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), and artificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole momen2024-01-01T00:00:00Z2024 Jul2026-06-03T00:39:21.163Z2025-02-19T02:27:15.919ZS-EPMC112932503908587010.1186/s13065-024-01248-6