Van-Thinh To, Tieu-Long Phan, Bao-Vy Ngoc Doan, Phuoc-Chung Van Nguyen, Quang-Huy Nguyen Le, Hoang-Huy Nguyen, The-Chuong Trinh, Tuyen Ngoc Truong
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Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.
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 compounds, 22 candidates were shortlisted. Among which (3S)-1-(4-acetylphenyl)-5-oxopyrrolidine-3-carboxylic acid emerged as the most promising.
期刊介绍:
Future Medicinal Chemistry offers a forum for the rapid publication of original research and critical reviews of the latest milestones in the field. Strong emphasis is placed on ensuring that the journal stimulates awareness of issues that are anticipated to play an increasingly central role in influencing the future direction of pharmaceutical chemistry. Where relevant, contributions are also actively encouraged on areas as diverse as biotechnology, enzymology, green chemistry, genomics, immunology, materials science, neglected diseases and orphan drugs, pharmacogenomics, proteomics and toxicology.