Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines

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.

KEYWORDS:

• Anticancer
• artificial neural network
• liver cancer
• fragment
• multi-target
• QSAR
• virtual design

Acknowledgements

The present work was carried out with the partial financial support of the Brazilian National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico − CNPq) under the grant numbers 309648/2019-0 and 431254/2018-4.

Disclosure statement

The authors declare no conflict of interest, financial or otherwise.

Supplementary material

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Additional information

Funding

This work was partially supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [309648/2019-0, 431254/2018-4].