Blackboard is a collaborative virtual learning tool used for higher learning that has been found to be an effective and efficient means of interactions between teachers and students and offers effective educational information management. The aim of this research work is to assess the preclinical and clinical dental students’ perception of Blackboard Collaborate as a quality teaching and learning tool as well as to find out areas that might appear as barriers to quality teaching and learning. This cross-sectional study was conducted online using survey monkey involving 245 dental students who had participated in the virtual classroom lectures during the pandemic with 18 students not completing the survey. The survey instrument was a nine-item questionnaire that included the age, sex, and year of study of the students as well as previous exposure to online lectures. The data collated was analyzed using IBM Statistical Package for the Social Sciences (SPSS) Statistics for windows version 22. Among 245 respondents that were enrolled in the study, 227 respondents completed the survey, of which 58.1% (n = 132) were male while 41.9% (n = 95) were females. Of the 227 respondents that completed this study, 74.8% (n = 170) of them experienced minimum to moderate technical problems regarding connectivity during the online sessions while 1.8% (n = 4) of the respondents experienced very severe technical problems. The majority of the respondents 54.2% (n = 123) support the continuation of online lectures even after the pandemic. In conclusion, we found a positive perception of our respondents to online lectures using Blackboard Collaborate. Internet connectivity as well as a decline in the comprehension of the lectures as compared to face-to-face learning were found as barriers to online learning.

Background: Mitogen-activated protein kinase 3 (MAPK3/ERK1) plays a central role in cellular proliferation, inflammation, apoptosis, and survival signalling and has been implicated in oral squamous cell carcinoma (OSCC), periodontitis, oral lichen planus, and other chronic oral inflammatory diseases. The present study employed an integrated computational workflow combining machine learning (ML)-based quantitative structure–activity relationship (QSAR) modelling, molecular docking, density functional theory (DFT), molecular dynamics (MD) simulation, and MM-GBSA analysis to identify and characterise potent MAPK3 inhibitors. Methods: A curated dataset of 907 experimentally validated MAPK3 inhibitors was retrieved from the ChEMBL database and processed using molecular descriptors and Morgan fingerprints. Multiple ML algorithms were evaluated under scaffold-based validation, with Light Gradient Boosting Machine (LightGBM) demonstrating the best predictive performance. Results: The final model achieved strong classification capability with ROC-AUC values of 0.898 and 0.926. Feature importance analysis revealed that local structural motifs captured by fingerprint descriptors played dominant roles in MAPK3 inhibitory activity. The top-ranked compounds were subjected to molecular docking, where compounds 58324148 and 137531515 exhibited strong binding affinities of −11.9 and −11.0 kcal/mol, respectively. DFT calculations demonstrated favourable electronic properties with low HOMO–LUMO energy gaps, while MD simulations confirmed stable receptor–ligand interactions throughout 200 ns trajectories. MM-GBSA analysis further supported strong binding stability dominated by van der Waals interactions. Conclusions: Overall, the integrated computational framework successfully identified promising MAPK3 inhibitor candidates with potential therapeutic relevance for oral inflammatory and proliferative diseases.