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

Overview

Journal Int J Mol Sci

Publisher MDPI

Specialties Biochemistry
Chemistry
Molecular Biology

Date 2024 Sep 14

PMID 39273338

Authors

Selma Zukic

Amar Osmanovic

Anja Harej Hrkac

Sandra Kraljevic Pavelic

Selma Spirtovic-Halilovic

Elma Veljovic

Suncica Roca

Snezana Trifunovic

Davorka Zavrsnik

Uko Maran

Affiliations

Soon will be listed here.

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.

Citing Articles

Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning.

Toots K, Sild S, Leis J, Acree W, Maran U Langmuir. 2024; 40(45):23714-23728.

PMID: 39473167 PMC: 11562803. DOI: 10.1021/acs.langmuir.4c02628.

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