Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.

ABSTRACT: Aims: Immune checkpoint inhibitors targeting PD-L1 are crucial in cancer research for preventing cancer cells from evading the immune system.Materials & methods: This study developed a screening model combining ANN, molecular similarity, and GNINA 1.0 docking to target PD-L1. A database of 2044 substances was compiled from patents.Results: For molecular similarity, the AVALON emerged as the most effective fingerprint, demonstrating an AUC-ROC of 0.963. The ANN model outperformed the Random Forest and Support Vector Classifier in cross-validation and external validation, achieving an average precision of 0.851 and an F1 score of 0.790. GNINA 1.0 was validated through redocking and retrospective control, achieving an AUC of 0.975.Conclusions: From 15235 DrugBank c

SUBMITTER: To VT 

PROVIDER: S-EPMC11559370 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature