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Original Article

J App Pharm Sci. 2023; 13(8): 177-184

Machine learning model for antiproliferative virtual screening of herbal compounds against hepatocellular carcinoma

Firdayani Firdayani, Agam Wira Sani, Maya Damayanti Rahayu, Arief Sartono, Galuh Widiyarti, Andini Sundowo, Damai Ria Setyawati.

Cited by 1 Articles

Machine learning has been applied to virtual screening in discovering novel antiproliferative agents against hepatocellular carcinoma (HCC) from bioactive herbal compounds. Machine learning models that have been performed to predict activities were constructed using extended connectivity fingerprints up to four bonds (ECFP4) to represent molecule structures. The dataset, consisting of 5460 molecules with antiproliferative activity against HepG2, was obtained from the ChEMBL database. An evaluation of several algorithms on the primary dataset revealed that random forest gave the best model, both regression and classification performance, with the coefficient determination of 0.803 and 0.954, respectively. Virtual screening results identified some bioactive compounds from the medicinal plants that are predicted to have potential activities as antiproliferative. This action can lessen the number of chemical samples that will be tried in the wet lab utilizing the HepG2 cells as an in vitro assay model. Along these lines, this action will reduce trial and error so more functional in time, cost, and exertion in disclosing anticancer medications.

Key words: machine learning; virtual screening, antiproliferative, hepatocellular carcinoma, herbal compounds, HepG2

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