Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity

Abstract

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal “leave some out” test. The developed model could be utilized in future preclinical experiments with novel drugs.

A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase

“…The comparative analysis provided in Table 3 reveals the significant advancements made in this work over prior QSAR studies focusing on FLT3 tyrosine kinase inhibitor compounds. Notably, the dataset size in the current study is at least 14 times larger than those used in previous research efforts, such as those by Kar et al [11], Shih et al [12], Abutayeh et al [13], Bhujbal et al [14], Fernandes et al [15], and Ghosh et al [16]. This substantial increase in dataset size to 1350 compounds, with a training set of 1080 and a testing set of 270, bolsters the statistical power of the study and provides a more comprehensive understanding of the molecular descriptors' impact on pIC50 values.…”

“…This substantial increase in dataset size to 1350 compounds, with a training set of 1080 and a testing set of 270, bolsters the statistical power of the study and provides a more comprehensive understanding of the molecular descriptors' impact on pIC50 values. [11][12][13][14][15][16].…”

“…The understanding and development of FLT3 inhibitors has greatly benefited from the implementation of Quantitative Structure-Activity Relationships (QSAR) and molecular docking [10][11][12][13][14][15][16]. The study by Sandoval et al [10] exemplifies the use of QSAR in predicting with notable accuracy the anti-leukemic activity of compounds, employing linear discriminant and multilinear regression analyses.…”

“…Similarly, Shih and Bhujbal et al [12,14] identified key structural features and designing novel compounds with enhanced FLT3 inhibitory activity by integrating molecular docking with 3D-QSAR approaches. Ghosh et al [16] further demonstrated the efficacy of computational modeling, including molecular dynamics and 3D-QSAR, in understanding the structure-activity relationship of FLT3 inhibitors. These methodologies, endorse by studies like those of Fernandes and Islam et al [15,17], have provided invaluable insights into the molecular interactions and binding affinities of potential FLT3 inhibitors, emphasizing the significance of these approaches in the realm of drug discovery and development for AML.…”