Related Experiment Video
Updated: Jun 5, 2026
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Drug repurposing of liver cancer based on multimodal neural network and virtual screening
Xu Huang1, Xiaoqian Jia1, Yu Zhou2
1Department of Marine Pharmacy, School of Life Science and Technology, China Pharmaceutical University.
Abstract:
Liver cancer is a malignant tumor with high incidence and mortality rates globally. Existing therapeutic methods have certain limitations in terms of efficacy, safety, and applicability, creating an urgent need for the development of new treatment strategies. Drug repurposing, which explores new indications for already approved drugs, offers significant advantages in reducing research and development costs and shortening development timelines. This study utilized a multimodal neural network model to integrate molecular fingerprints and molecular graphs to construct a drug-target interaction prediction system for liver cancer targets. A large-scale virtual screening of The US Food and Drug Administration-approved drugs was conducted, followed by molecular docking and molecular dynamics simulations to analyze binding stability. The screening identified 17 potential antiliver cancer drugs. Further verification of their in-vitro activity was performed through 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and wound healing assays in MHCC97-H, HepG2, and Huh7 cell lines, revealing that Manidipine and Vildagliptin exhibited significant cytotoxicity and migration inhibition. These results demonstrate the feasibility of the proposed screening workflow and provide a set of candidate molecules for further mechanistic investigation in liver cancer drug repurposing.
Insights
Drug repurposing identified new liver cancer treatments. A multimodal neural network screened FDA-approved drugs, finding Manidipine and Vildagliptin effective against liver cancer cells in vitro.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- Liver cancer presents high global incidence and mortality.
- Current treatments face limitations in efficacy, safety, and applicability.
- Drug repurposing offers a cost-effective and time-efficient strategy for discovering new therapies.
Purpose of the Study:
- To develop a drug-target interaction prediction system for liver cancer.
- To identify potential anti-liver cancer drugs through virtual screening of FDA-approved medications.
- To validate the efficacy of candidate drugs using in vitro assays.
Main Methods:
- A multimodal neural network integrated molecular fingerprints and graphs for drug-target prediction.
- Large-scale virtual screening of FDA-approved drugs against liver cancer targets.
- Molecular docking and dynamics simulations assessed binding stability.
- In vitro cytotoxicity (MTT assay) and migration inhibition (wound healing assay) were performed on selected cell lines (MHCC97-H, HepG2, Huh7).
Main Results:
- The study identified 17 potential anti-liver cancer drug candidates.
- Manidipine and Vildagliptin demonstrated significant cytotoxicity against liver cancer cell lines.
- Both Manidipine and Vildagliptin inhibited cancer cell migration.
- The screening workflow proved effective in identifying promising drug repurposing candidates.
Conclusions:
- The proposed computational workflow is feasible for liver cancer drug repurposing.
- Manidipine and Vildagliptin are promising candidates for further investigation as liver cancer therapeutics.
- This study provides a valuable set of molecules for future mechanistic studies in liver cancer treatment.
