Zukić, S.; Osmanović, A.; Harej, A.; Kraljević Pavelić, S.; Špirtović-Halilović, S.; Veljović, E.; Roca, S.; Trifunović, S.; Završnik, D.; Maran, U. Data driven modelling of substituted pyrimidine and uracil-based derivatives validated with newly synthesized and antiproliferative evaluated compounds. Int. J. Mol. Sci. 2024, 25, 9390

Abstract

The pyrimidine heterocycle plays an important role in anticancer research. In particular, the py-rimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning QSAR models 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 has been collected and curated. A workflow was developed for data-driven machine learning QSAR using 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 com-pound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with QSAR model with differences of 0.02 compared to experimentally tested activity.