Linear Residual Network Modeling for Anti-HIV-1 Activity Prediction and Docking-Validated Design of Biphenyl-DAPY-Based NNRTIs

To predict the anti-HIV-1 activity of biphenyl-DAPY-based non-nucleoside reverse transcriptase inhibitors (NNRTIs), a quantitative structure–activity relationship (QSAR) analysis was conducted. Using the heuristic method (HM) for descriptor selection, four predictive models were established: support vector regression (SVR), kernel ridge regression, linear mixed-kernel SVR, and Linear Residual Network (LRNet). Rigorous validations, including leave-one-out cross-validation, fivefold cross-validation, and Y-randomization tests, confirmed their reliability. The LRNet model exhibited the best performance, achieving an average training set R2 of 0.8838±0.0090 and an average test set R2 of 0.9026±0.0187 over 50 random train–test splits, with Q5−fold2 and QLOO2 being 0.8533 and 0.8541, respectively. To further verify the robustness and generalizability of LRNet, independent validation was performed using an external dataset, where LRNet also achieved better generalization performance. The HM and LRNet models were employed to guide the design of novel compounds. Their favorable binding modes with the 1RT2 protein and pharmacokinetic properties were verified via molecular docking and in silico ADMET profiling, respectively. Furthermore, 100 ns molecular dynamics simulations demonstrated the robust dynamic stability, structural compactness, and thermodynamic convergence of the designed candidate within the 1RT2 binding pocket. This study provides a useful computational framework for the rational design and activity prediction of biphenyl-DAPY-based NNRTIs.