In silico study on the cytotoxicity against Hela cancer cells of xanthones bioactive compounds from Garcinia cowa: QSAR based on Graph Deep Learning, Network Pharmacology, and Molecular Docking
Nguyen Manh Son, Pham Huu Vang, Nguyen Thi Dung, Nguyen Manh Ha. Ta Thi Thao, Tran Thi Thu Thuy, Phan Minh Giang

TL;DR
This study combines QSAR, deep learning, network pharmacology, and molecular docking to predict and analyze the cytotoxic effects of Garcinia cowa xanthones on HeLa cancer cells, identifying key compounds and targets.
Contribution
It introduces a novel integrated approach using Graph Deep Learning and network pharmacology to evaluate bioactive compounds' anti-cancer potential.
Findings
Graph Attention Network achieved R2 of 0.98 in predicting pIC50.
Network pharmacology identified key compounds and protein targets.
Molecular docking suggested MTOR as a potential target for cytotoxicity.
Abstract
Cancer is recognized as a complex group of diseases, contributing to the highest global mortality rates, with increasing prevalence and a trend toward affecting younger populations. It is characterized by uncontrolled proliferation of abnormal cells, invasion of adjacent tissues, and metastasis to distant organs. Garcinia cowa, a traditional medicinal plant widely used in Southeast Asia, including Vietnam, is employed to treat fever, cough, indigestion, as a laxative, and for parasitic diseases. Numerous xanthone compounds isolated from this species exhibit a broad spectrum of biological activities, with some showing promise as anti cancer and antimalarial agents. Network pharmacology analysis successfully identified key bioactive compounds Rubraxanthone, Garcinone D, Norcowanin, Cowanol, and Cowaxanthone alongside their primary protein targets (TNF, CTNNB1, SRC, NFKB1, and MTOR),…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
