Data-Driven Modelling of Substituted Pyrimidine and Uracil-Based Derivatives Validated with Newly Synthesized and Antiproliferative Evaluated Compounds

Zukić, Selma; Osmanović, Amar; Harej Hrkać, Anja; Kraljević Pavelić, Sandra; Špirtović-Halilović, Selma; Veljović, Elma; Roca, Sunčica; Trifunović, Snežana; Završnik, Davorka; Maran, Uko (2024) Data-Driven Modelling of Substituted Pyrimidine and Uracil-Based Derivatives Validated with Newly Synthesized and Antiproliferative Evaluated Compounds. International Journal of Molecular Sciences, 25 (17). ISSN 1422-0067

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Abstract

The pyrimidine heterocycle plays an important role in anticancer research. In particular, the pyrimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning quantitative structure-activity relationships (QSARs) that allow for generalization and predictive capabilities in the search for new active compounds. To achieve this, a dataset of pyrimidine and uracil compounds from ChEMBL were collected and curated. A workflow was developed for data-driven machine learning QSAR using an intuitive dataset design and forwards selection of molecular descriptors. The model was thoroughly externally validated against available data. Blind validation was also performed by synthesis and antiproliferative evaluation of new synthesized uracil-based and pyrimidine derivatives. The most active compound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with the QSAR model with differences of 0.02 compared to experimentally tested activity.

Item Type:	Article
Uncontrolled Keywords:	QSAR; synthesis; antiproliferative activity; HeLa cell line; pyrimidines; uracil derivatives; drug design
Subjects:	NATURAL SCIENCES > Biology BIOMEDICINE AND HEALTHCARE
Divisions:	NMR Center
Depositing User:	Ana Zečević
Date Deposited:	02 Sep 2024 07:40
URI:	http://fulir.irb.hr/id/eprint/9038
DOI:	10.3390/ijms25179390

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