Related Experiment Video
Updated: Aug 19, 2026
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines
V V Kleandrova1, M T Scotti2, L Scotti2
1Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production , Moscow, Russian Federation.
Abstract:
Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.
Insights
Researchers developed a novel cell-based multi-target quantitative structure-activity relationship (CBMT-QSAR) model to discover new liver cancer treatments. This approach accelerates the design of effective anticancer agents, showing over 80% accuracy in predictions.
Area of Science:
- Oncology
- Medicinal Chemistry
- Computational Biology
Background:
- Liver cancer remains a leading cause of cancer-related mortality.
- Current chemotherapy for liver cancer faces challenges in efficacy and safety.
- Novel therapeutic strategies are urgently needed to combat liver malignancies.
Purpose of the Study:
- To develop the first cell-based multi-target quantitative structure-activity relationship (CBMT-QSAR) model for liver cancer.
- To design and predict novel chemical entities with anticancer activity against liver cancer cell lines.
- To utilize computational approaches for accelerating the discovery of new anti-liver cancer agents.
Main Methods:
- Development of a CBMT-QSAR model incorporating 17 liver cancer cell lines.
- Validation of the model for quality and predictive power (accuracy >80%).
- Application of the model to extract key molecular fragments for drug design.
Main Results:
- The CBMT-QSAR model demonstrated high accuracy in training and test sets.
- Physicochemical and structural interpretations of molecular descriptors were used to identify crucial fragments.
- Eight drug-like molecules were virtually designed, with six predicted as potent anticancer agents against the 17 cell lines.
Conclusions:
- The developed CBMT-QSAR model is a valuable tool for designing novel anticancer agents against liver cancer.
- This computational approach facilitates the identification of promising drug candidates with improved efficacy and safety profiles.
- The study highlights the potential of fragment-based drug design guided by QSAR modeling for oncology drug discovery.
